CN113361794A - Information pushing method and AI (Artificial Intelligence) pushing system based on Internet e-commerce big data - Google Patents

Information pushing method and AI (Artificial Intelligence) pushing system based on Internet e-commerce big data Download PDF

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CN113361794A
CN113361794A CN202110687437.7A CN202110687437A CN113361794A CN 113361794 A CN113361794 A CN 113361794A CN 202110687437 A CN202110687437 A CN 202110687437A CN 113361794 A CN113361794 A CN 113361794A
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刘彩虹
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Shenzhen Hongye Wire Co ltd
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Abstract

The embodiment of the application provides an information pushing method and an AI (Artificial Intelligence) pushing system based on Internet e-commerce big data, a content session activity event set is further considered on the basis of a pushed content page flow aiming at information display, then information pushing optimization information is obtained by predicting a potential interest activity flow included by each content session activity event and flow trigger information corresponding to each potential interest activity flow, and a current information pushing strategy of an e-commerce use terminal is optimized based on the information pushing optimization information, so that not only the potential interest activity flow is considered, but also the flow trigger information corresponding to the potential interest activity flow is considered, more information pushing optimization dimensions are increased, and the accuracy of a subsequent information pushing strategy is higher.

Description

Information pushing method and AI (Artificial Intelligence) pushing system based on Internet e-commerce big data
Technical Field
The application relates to the technical field of electronic commerce, in particular to an information pushing method and an AI pushing system based on Internet e-commerce big data.
Background
An Online-to-Offline (O2O for short) electronic commerce mode is a multilateral platform commerce mode for connecting Online users and Offline merchants. The O2O business model fuses the entity economy with online resources, so that the network becomes a channel for extending the entity economy to the virtual world; an offline business may go online to mine and attract customer sources, while a consumer may screen goods and services online and complete payment, and then go to a brick and mortar store for the remainder of the consumption. Based on the above, the big data mining becomes an important means for converting O2O electric commercial user data into valuable knowledge, and is a technology for searching the rule from a data sea by analyzing mass data.
In the related art, one branch of data mining for e-commerce behavior is to dig out user intentions, and then perform related content push to achieve content conversion. To enable the push-cast process, the current presentation of the push content page flow continues to further mine the information of potential interest in the content session activity, and then performs a second sub-optimization to account for feature information in more temporal dimensions. However, in the current information pushing optimization scheme, only the potential interest activity flow is usually considered, and the flow trigger information corresponding to the potential interest activity flow is not considered, so that the information pushing optimization dimension is too single, and the accuracy of the subsequent information pushing strategy is affected.
Disclosure of Invention
In order to overcome at least the above disadvantages in the prior art, an object of the present application is to provide an information pushing method and an AI pushing system based on internet e-commerce big data.
In a first aspect, the present application provides an information pushing method based on internet e-commerce big data, which is applied to an AI pushing system, where the AI pushing system is in communication connection with a plurality of e-commerce use terminals, and the method includes:
acquiring a target mining intention sequence of the target e-commerce behavior big data of the e-commerce using terminal, and displaying information on the e-commerce using terminal according to the target mining intention sequence;
acquiring a content session activity event set of a pushed content page flow shown by the information by the electronic commerce use terminal, wherein the content session activity event set comprises a plurality of content session activity events;
predicting potential interest activity processes included in each content session activity event and process triggering information corresponding to each potential interest activity process according to a pre-trained potential interest activity prediction model;
and generating information pushing optimization information according to the potential interest activity flow included by each content session activity event and the flow trigger information corresponding to each potential interest activity flow, and optimizing the current information pushing strategy of the electronic commerce use terminal based on the information pushing optimization information.
In a second aspect, an embodiment of the present application further provides an information push system based on internet e-commerce big data, where the information push system based on internet e-commerce big data includes an AI push system and a plurality of e-commerce use terminals communicatively connected to the AI push system;
the AI push system is configured to:
acquiring a target mining intention sequence of the target e-commerce behavior big data of the e-commerce using terminal, and displaying information on the e-commerce using terminal according to the target mining intention sequence;
acquiring a content session activity event set of a pushed content page flow shown by the information by the electronic commerce use terminal, wherein the content session activity event set comprises a plurality of content session activity events;
predicting potential interest activity processes included in each content session activity event and process triggering information corresponding to each potential interest activity process according to a pre-trained potential interest activity prediction model;
and generating information pushing optimization information according to the potential interest activity flow included by each content session activity event and the flow trigger information corresponding to each potential interest activity flow, and optimizing the current information pushing strategy of the electronic commerce use terminal based on the information pushing optimization information.
Based on any one of the above aspects, the content session activity event set is further considered on the basis of the pushed content page flow for information display, then the optimization information is pushed by predicting the potential interest activity flow included in each content session activity event and the flow trigger information corresponding to each potential interest activity flow, and the current information pushing strategy of the electronic commerce use terminal is optimized based on the information pushing optimization information, so that not only the potential interest activity flow itself but also the flow trigger information corresponding to the potential interest activity flow are considered, and more information pushing optimization dimensions are increased, so that the accuracy of the subsequent information pushing strategy is higher.
Drawings
Fig. 1 is a schematic application scenario diagram of an information push system based on internet e-commerce big data according to an embodiment of the present application;
fig. 2 is a schematic flowchart of an information pushing method based on internet e-commerce big data according to an embodiment of the present application;
fig. 3 is a schematic block diagram of structural components of an AI pushing system for implementing the above-described information pushing method based on internet e-commerce big data according to the embodiment of the present application.
Detailed Description
The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.
Fig. 1 is an interaction diagram of an information push system 10 based on internet e-commerce big data according to an embodiment of the present application. The internet e-commerce big data based information push system 10 may include an AI push system 100 and an e-commerce usage terminal 200 communicatively connected to the AI push system 100. The internet e-commerce big data based information push system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the internet e-commerce big data based information push system 10 may also include only at least some of the components shown in fig. 1 or may also include other components.
In an embodiment that can be implemented independently, the AI pushing system 100 and the e-commerce using terminal 200 in the internet e-commerce big data based information pushing system 10 can cooperate to execute the internet e-commerce big data based information pushing method described in the following method embodiment, and the detailed description of the method embodiment can be referred to in the following steps of the AI pushing system 100 and the e-commerce using terminal 200.
To solve the technical problem in the foregoing background art, fig. 2 is a schematic flow chart of an information pushing method based on internet e-commerce big data according to an embodiment of the present application, where the information pushing method based on internet e-commerce big data according to the present embodiment may be executed by the AI pushing system 100 shown in fig. 1, and the information pushing method based on internet e-commerce big data is described in detail below.
And step S110, acquiring a target mining intention sequence of the target e-commerce behavior big data of the e-commerce using terminal, and displaying information on the e-commerce using terminal according to the target mining intention sequence.
Step S120, acquiring a content session activity event set of the e-commerce use terminal aiming at the pushed content page flow displayed by the information.
In this embodiment, the content session activity event set may include a plurality of content session activity events. Each content session activity event may be used to characterize session activity behavior data for the pushed content page flow presented for information, such as, but not limited to, session initiation behavior data, session sharing behavior data, session response behavior data, and the like, where the session activity behavior data may reflect an operation preference or interest of a user of the e-commerce terminal for the currently pushed content page flow, that is, the session activity behavior data may have certain potential interest information.
Step S130, predicting the potential interest activity flow included in each content session activity event and the flow trigger information corresponding to each potential interest activity flow according to the pre-trained potential interest activity prediction model.
Step S140, generating information pushing optimization information according to the potential interest activity flow included in each content session activity event and the flow trigger information corresponding to each potential interest activity flow, and optimizing the current information pushing policy of the e-commerce use terminal based on the information pushing optimization information. For example, the pushing of the related content may be performed based on the optimized information pushing policy.
Based on the above steps, the embodiment further considers the content session activity event set on the basis of the pushed content page flow for information display, then information pushes optimization information by predicting the potential interest activity flow included in each content session activity event and the flow trigger information corresponding to each potential interest activity flow, and optimizes the current information push strategy of the e-commerce use terminal based on the information push optimization information, so that not only the potential interest activity flow itself but also the flow trigger information corresponding to the potential interest activity flow are considered, and more information push optimization dimensions are increased, so that the accuracy of the subsequent information push strategy is higher.
In a separate embodiment, step S130 can be implemented by the following exemplary steps.
Step S131, extracting content session activity characteristics corresponding to each content session activity event according to a pre-trained potential interest activity prediction model, and predicting potential interest prediction information corresponding to the content session activity events based on the content session activity characteristics, wherein the potential interest prediction information includes a prediction confidence of the content session activity events for each candidate potential interest activity process.
Step S132, obtaining the potential interest activity flow included in each content session activity event based on the potential interest prediction information corresponding to the content session activity event.
Step S133, acquiring process trigger information corresponding to the potential interest activity process included in each content session activity event.
Based on the above description, in an independently implementable embodiment, the disclosed embodiment provides an interest activity prediction method based on artificial intelligence, comprising the following steps.
Step S101, acquiring a reference content session activity event set, wherein the reference content session activity event set comprises a plurality of reference content session activity events and reference potential interest information corresponding to each reference content session activity event;
step S102, inputting each reference content session activity event in the reference content session activity event set into an initial potential interest activity prediction model, and obtaining potential interest prediction information corresponding to each reference content session activity event;
step S103, optimizing model weight parameters of the initial potential interest activity prediction model based on the reference potential interest information and the potential interest prediction information, judging whether the initial potential interest activity prediction model meets a model convergence requirement, outputting a potential interest activity prediction model obtained by training when the initial potential interest activity prediction model meets the model convergence requirement, and returning to execute the step of inputting each reference content session activity event in a reference content session activity event set into the initial potential interest activity prediction model to obtain potential interest prediction information corresponding to each reference content session activity event when the initial potential interest activity prediction model does not meet the model convergence requirement.
In a separate embodiment, step S140 can be implemented by the following exemplary steps.
Step S141, determining a sequence of potential interest portraits of each content session activity event according to the process log information of the potential interest activity process included in each content session activity event.
Step S142, determining a flow trigger portrait sequence of each content session activity event according to flow trigger information corresponding to a potential interest activity flow included in each content session activity event.
And step S143, fusing the process triggering image sequence and the potential interest image sequence to obtain a fused potential interest image sequence.
And step S144, generating information pushing optimization information based on the fusion potential interest portrait sequence, and optimizing the current information pushing strategy of the electronic commerce use terminal based on the information pushing optimization information.
For example, step S144 can be implemented by the following exemplary embodiments.
(1) Acquiring content hotspot distribution information corresponding to the fusion potential interest portrait sequence, and acquiring the distribution heat value of each content hotspot in the content hotspot distribution information;
(2) summarizing a key content hotspot distribution sequence from the content hotspot distribution information according to the distributed heat value of each content hotspot;
(3) determining candidate content hotspot distribution in the content hotspot distribution information and hotspot tracking cost values of the candidate content hotspot distribution based on the key content hotspot distribution sequence;
(4) obtaining a push content hotspot distribution set in the content hotspot distribution information according to content hotspot distributions except the candidate content hotspot distribution in the content hotspot distribution information and hotspot tracking relations among the content hotspot distributions;
(5) determining hotspot tracking cost values of the distribution of each content hotspot in the push content hotspot distribution set based on the push content hotspot distribution set and the candidate content hotspot distribution; and the determined hotspot tracking cost value is used for generating information push optimization information corresponding to the distribution of the corresponding content hotspots. For example, an information push optimization index corresponding to a hotspot tracking cost value can be obtained based on a pre-configured mapping relationship, and then an information push optimization index corresponding to corresponding content hotspot distribution can be obtained, and then information push optimization information corresponding to corresponding content hotspot distribution can be obtained, and then the current information push strategy of the e-commerce use terminal can be optimized according to the information push optimization information corresponding to corresponding content hotspot distribution, for example, the weight information of each information push object in the current information push strategy can be optimized, and then the optimization of final push content is realized by adjusting different information push objects, so as to match the current potential interest portrait information.
For example, in a possible implementation manner, the obtaining the heat value of each content hotspot distribution in the content hotspot distribution information includes: acquiring the content hotspot distribution information; determining the content hotspot relevancy of the associated content hotspot distribution of each content hotspot distribution in the content hotspot distribution information; and taking the content hotspot relevance of the distribution of the associated content hotspots as the heat value of the distribution of the corresponding content hotspots.
For example, in a possible implementation manner, the summarizing a key content hotspot distribution sequence from the content hotspot distribution information according to the distributed heat value of each content hotspot includes: acquiring a preset target heat value; and removing the content hotspot distribution with the thermal value smaller than or equal to the target thermal value and the hotspot tracking relation corresponding to the content hotspot distribution from the content hotspot distribution information, and obtaining a key content hotspot distribution sequence according to the candidate content hotspot distribution in the content hotspot distribution information and the hotspot tracking relation between the candidate content hotspot distributions.
For example, in one possible implementation, the determining the candidate content hotspot distribution and the hotspot tracking cost value of the candidate content hotspot distribution in the content hotspot distribution information based on the key content hotspot distribution sequence includes: according to the content hotspot relevancy of the content hotspot distribution in the key content hotspot distribution sequence associated with the content hotspot distribution, obtaining the thermal value of each content hotspot distribution in the key content hotspot distribution sequence, and taking the thermal value in the key content hotspot distribution sequence as the initial current hotspot tracking cost value of the corresponding content hotspot distribution; executing each content hotspot distribution in the key content hotspot distribution sequence in a traversing manner, and determining a balance heat value corresponding to the content hotspot distribution according to the current hotspot tracking cost value of the content hotspot distribution in the key content hotspot distribution sequence;
removing the content hotspot distribution from the key content hotspot distribution sequence when the trade-off thermal force value is less than or equal to a preset target thermal force value;
when the weighted thermal value is larger than the target thermal value and smaller than the current hotspot tracking cost value of the content hotspot distribution, optimizing the current hotspot tracking cost value of the content hotspot distribution according to the weighted thermal value of the content hotspot distribution until the current hotspot tracking cost value of each content hotspot distribution in the key content hotspot distribution sequence is not optimized in the secondary traversal process, and terminating the traversal;
taking content hotspot distribution in a key content hotspot distribution sequence obtained during the ending traversal as the candidate content hotspot distribution, and taking the current hotspot tracking cost value of the candidate content hotspot distribution during the ending traversal as the hotspot tracking cost value corresponding to the candidate content hotspot distribution;
on the basis of the above description, after the next traversal is finished, recording content hotspot distribution with the optimized current hotspot tracking cost value in the current traversal process, wherein the recorded content hotspot distribution is used for indicating that the recorded content hotspot distribution is distributed in the associated content hotspot distribution in the key content hotspot distribution sequence when the next traversal starts, and is used as target content hotspot distribution with the need of re-determining the weighted heat value in the next traversal process;
for example, in one possible implementation, the determining, for each content hotspot distribution in the key content hotspot distribution sequence, a weighted heat value corresponding to the content hotspot distribution according to a current hotspot tracking cost value of the content hotspot distribution in the associated content hotspot distribution in the key content hotspot distribution sequence includes:
and for target content hotspot distribution in the key content hotspot distribution sequence, determining a balance heat value corresponding to the target content hotspot distribution according to the current hotspot tracking cost value of the target content hotspot distribution in the associated content hotspot distribution in the key content hotspot distribution sequence.
For example, in a possible implementation manner, the obtaining a push content hotspot distribution set in the content hotspot distribution information according to the content hotspot distributions in the content hotspot distribution information except the candidate content hotspot distribution and a hotspot tracking relationship between the content hotspot distributions includes:
removing the candidate content hotspot distribution from the content hotspot distribution information;
and obtaining a push content hotspot distribution set according to the candidate content hotspot distribution after the candidate content hotspot distribution is removed and the hotspot tracking relation between the candidate content hotspot distributions.
For example, in one possible implementation, the determining a hotspot tracking cost value of each content hotspot distribution in the pushed content hotspot distribution set based on the pushed content hotspot distribution set and the candidate content hotspot distribution includes:
resetting the current hotspot tracking cost value of each content hotspot distribution in the push content hotspot distribution set according to the content hotspot relevancy of each content hotspot distribution in the push content hotspot distribution set in the historical content hotspot distribution information associated with the content hotspot distribution;
executing each content hotspot distribution in the push content hotspot distribution set in a traversing manner, and determining a balance heat value corresponding to the content hotspot distribution according to the current hotspot tracking cost value of the content hotspot distribution in the content hotspot distribution information, which is related to the content hotspot distribution;
when the weighted heat value is smaller than the current hotspot tracking cost value of the content hotspot distribution, optimizing the current hotspot tracking cost value of the content hotspot distribution according to the weighted heat value of the content hotspot distribution until the current hotspot tracking cost value of each content hotspot distribution in the push content hotspot distribution set is not optimized in the next traversal process, and terminating the traversal;
taking a current hotspot tracking cost value of the content hotspot distribution after the traversal as a hotspot tracking cost value corresponding to the content hotspot distribution;
for example, in one possible implementation, the method further comprises:
after the current traversal is finished, recording the content hotspot distribution with the optimized current hotspot tracking cost value in the current traversal process;
the recorded content hotspot distribution is used for indicating that the recorded content hotspots are distributed in the associated content hotspot distribution in the pushed content hotspot distribution set when the next traversal starts, and the associated content hotspot distribution is used as the target content hotspot distribution of which the weighted heat value needs to be determined again in the next traversal process;
for each content hotspot distribution in the pushed content hotspot distribution set, determining a weighted heat value corresponding to the content hotspot distribution according to a current hotspot tracking cost value of the content hotspot distribution in the content hotspot distribution information associated with the content hotspot distribution, including:
and for the target content hotspot distribution in the push content hotspot distribution set, determining a balance heat value corresponding to the target content hotspot distribution according to the current hotspot tracking cost value of the target content hotspot distribution in the content hotspot distribution information and associated content hotspot distribution.
For example, in one possible implementation, the determining the weighted heat value corresponding to the content hotspot distribution includes:
if the content hotspot distribution meets the condition that the current hotspot tracking cost value of x associated content hotspot distributions in the associated content hotspot distribution is greater than or equal to x and does not meet the condition that the current hotspot tracking cost value of x +1 associated content hotspot distributions is greater than or equal to x +1, determining the balance heat value corresponding to the content hotspot distribution as x, wherein x is a positive integer;
correspondingly, the method further comprises the following steps:
when the traversal process is started, resetting the optimization times of content hotspot distribution to be zero, wherein the optimization times of the content hotspot distribution are used for recording the content hotspot correlation of the content hotspot distribution with the optimized current hotspot tracking cost value in the traversal process;
counting the content hotspot relevance of the content hotspot distribution with the optimized current hotspot tracking cost value in the current traversal process;
optimizing the optimization times of the content hotspot distribution according to the content hotspot relevancy;
on the premise that the traversal process is finished, if the optimization times of the content hotspot distribution are nonzero, continuing the next traversal process;
and on the premise that the traversal process is finished, if the optimization times of the content hotspot distribution are zero, the traversal is terminated.
In an embodiment, in step S110, in the process of obtaining the target mining intention sequence of the target e-commerce behavior big data of the e-commerce use terminal and displaying information on the e-commerce use terminal according to the target mining intention sequence, the following steps may be implemented.
Step A110, a target e-commerce behavior data set and an e-commerce intention mining network to be configured in a convergence mode are obtained, the target e-commerce behavior data set comprises target e-commerce behavior data, target e-commerce intention information corresponding to the target e-commerce behavior data and reference e-commerce behavior data, the e-commerce intention mining network comprises a first vector mining unit, a second vector mining unit and a vector recovery unit, and the first vector mining unit and the second vector mining unit share a configuration vector compression unit.
The specific category of the target e-commerce behavior data is not limited, and the target e-commerce behavior data may be e-commerce live broadcast behavior data, or e-commerce order line data, and the like. In an embodiment that can be implemented independently, the target e-commerce behavior data may also be obtained by intercepting part of the e-commerce behavior big data. The target e-commerce intention information corresponding to the target e-commerce behavior data may include a target mining intention sequence corresponding to the target e-commerce behavior data, which may specifically be a prediction confidence that the target e-commerce behavior data belongs to each candidate e-commerce intention.
Before the comparison e-commerce behavior data is not updated, the comparison e-commerce behavior data may be blank e-commerce behavior data or random noise e-commerce behavior data, and is not particularly limited. The blank e-commerce behavior data is the e-commerce behavior data of which the content data of all e-commerce behavior items are empty sets.
The e-commerce intent mining network may be an artificial intelligence network model that utilizes back-propagation algorithms to make output features equal to input features. The vector compression unit may be an encoder (encoder) and the vector restoration unit may be a decoder (decoder).
Wherein the vector compression unit may compress the input sequence of behavior vectors into a potential distribution vector. The vector restoration unit may reconstruct the input from the potential distribution vector.
In an embodiment that can be implemented independently, the e-commerce intention mining network to be configured in a converged manner may be preliminarily trained based on target e-commerce behavior data, specifically, the target e-commerce behavior data is subjected to vector mining through a first vector mining unit and a second vector mining unit to obtain a first behavior vector sequence and a second behavior vector sequence of the target e-commerce behavior data, then, the target e-commerce behavior data is reconstructed through a vector recovery unit based on the first behavior vector sequence and the second behavior vector sequence to obtain target e-commerce behavior data after vector recovery, and based on a training convergence evaluation value between the target e-commerce behavior data after vector recovery and original target e-commerce behavior data, unit weight information of the e-commerce intention mining network is adjusted to obtain the preliminarily trained e-commerce intention mining network, and further, the e-commerce intention mining network is further trained through the following embodiments.
For example, the E-business intention mining network is composed of a vector compression unit, a vector recovery unit, a first vector mining subunit and a second vector mining subunit, wherein the vector compression unit and the first vector mining subunit form the first vector mining unit, the vector compression unit and the second vector mining subunit form the second vector mining unit, and the first vector mining unit and the second vector mining unit share one vector compression unit. In an embodiment, which may be implemented independently, the first vector mining subunit and the second vector mining subunit may be considered as part of a vector compression unit, i.e. the first vector mining unit and the second vector mining unit may belong to a vector compression unit.
Based on the correlation scheme, the training scheme of the E-business intention mining network comprises two training schemes. One method is that target e-commerce intention information corresponding to e-commerce behavior data is directly utilized, a supervised mode is used for training an e-commerce intention mining network, and the e-commerce intention mining network learns a vector sequence strongly related to the target e-commerce intention information; the other method is that an E-business intention mining network is initially trained, a vector sequence which is as rich as possible is extracted by using the property that the E-business intention mining network reconstructs original E-business behavior data, and then an intention mining unit is connected to learn and predict intention mining characteristics based on the vector sequence. However, when deep association vectors exist in a target e-commerce behavior data set, the deep association vectors are ignored by the first method, so that the prediction accuracy of the e-commerce intention mining network obtained through training is low, and it cannot be guaranteed that the e-commerce intention mining network can learn the deep association vectors through training by the second scheme.
For example, assuming that there is a target e-commerce behavior data set, the target e-commerce behavior data set may be divided into two target e-commerce behavior data subsets, where a, B, and the behavior vector in the entire target e-commerce behavior data set is only composed of 4 behavior vectors, which are respectively behavior vector 1, behavior vector 2, behavior vector 3, and behavior vector 4; the e-commerce behavior data of each of the a-labeled e-commerce behavior data subsets comprises a behavior vector 1 and a behavior vector 2, the e-commerce behavior data of each of the B-labeled e-commerce behavior data subsets comprises a behavior vector 3 and a behavior vector 4, then the behavior vector 1 and the behavior vector 2 are depth association vectors, and the behavior vector 3 and the behavior vector 4 are depth association vectors.
Step A120, performing initial training on the first vector mining unit based on the target e-commerce behavior data and the corresponding target e-commerce intention information to obtain a first vector mining unit after the initial training, wherein the first vector mining unit comprises a vector compression unit and a first vector mining subunit.
In an embodiment, the step of "initially training the first vector mining unit based on the target e-commerce behavior data and the corresponding target e-commerce intention information to obtain an initially trained first vector mining unit" may include:
vector mining is carried out on the target e-commerce behavior data through the first vector mining unit, and a first behavior vector sequence of the target e-commerce behavior data is obtained;
performing intention mining on the target e-commerce behavior data based on a first behavior vector sequence of the target e-commerce behavior data to obtain an actual first prediction confidence coefficient that the target e-commerce behavior data belongs to a candidate e-commerce intention;
and optimizing unit importance coefficient information of the first vector mining unit based on the actual first prediction confidence coefficient and the target E-commerce intention information to obtain the initially trained first vector mining unit.
The target e-commerce behavior data are subjected to vector mining through the first vector mining unit, the target e-commerce behavior data can be subjected to vector compression through the vector compression unit, and then the target e-commerce behavior data subjected to vector compression are subjected to vector mining through the first vector mining subunit to obtain a first behavior vector sequence of the target e-commerce behavior data.
After the first behavior vector sequence of the target e-commerce behavior data is extracted through the first vector mining unit, a first intention mining unit can be added into the e-commerce intention mining network, the first intention mining unit serves as an output node of the first vector mining unit, the first intention mining unit takes the extracted first behavior vector sequence as an input node, intention mining is carried out on the target e-commerce behavior data on the basis of the first behavior vector sequence, and an actual first prediction confidence coefficient that the target e-commerce behavior data belongs to candidate e-commerce intentions is obtained. The candidate e-commerce intentions may include a plurality of e-commerce intentions, and the target e-commerce behavior data is subjected to intention mining, so that an actual first prediction confidence that the target e-commerce behavior data belongs to each candidate e-commerce intention can be obtained.
The first intention mining unit may be, for example, a recurrent neural network or a fully-connected deep neural network, and the like, which is not limited in this embodiment.
The target e-commerce intention information of the target e-commerce behavior data is specifically a prediction confidence coefficient that the target e-commerce behavior data belongs to the candidate e-commerce intention; the step of optimizing unit importance coefficient information of the first vector mining unit based on the actual first prediction confidence and the target e-commerce intention information to obtain the initially trained first vector mining unit may specifically include:
calculating a second training convergence evaluation value between the actual first prediction confidence and the prediction confidence;
and optimizing the unit importance coefficient information of the first vector mining unit based on the second training convergence evaluation value so as to make the second training convergence evaluation value converge and obtain the first vector mining unit after initial training.
Wherein the preset condition may be that the second training convergence evaluation value is smaller than a preset evaluation value.
For example, the unit importance coefficient information of the first vector mining unit may be optimized through a back propagation algorithm, and the unit weight information of the first vector mining unit may be optimized based on the second training convergence evaluation value, so that the actual first prediction confidence that the target e-commerce behavior data belongs to the candidate e-commerce intention approaches the prediction confidence.
In an embodiment, the unit importance coefficient information in the first vector mining unit and the first intention mining unit may also be optimized based on the second training convergence evaluation value to obtain the first vector mining unit and the first intention mining unit after the initial training.
Step a130, performing vector mining on the target e-commerce behavior data and the comparison e-commerce behavior data through the initially trained first vector mining unit to obtain a target behavior vector sequence corresponding to the target e-commerce behavior data and a comparison behavior vector sequence corresponding to the comparison e-commerce behavior data.
After the first vector mining unit is initially trained based on the target e-commerce intention information, the initially trained first vector mining unit may extract a behavior vector sequence that is strongly correlated with the target e-commerce intention information.
The comparison e-commerce behavior data may specifically be blank e-commerce behavior data or random noise e-commerce behavior data.
Step a140, updating the comparison e-commerce behavior data based on the training convergence evaluation value between the target behavior vector sequence and the comparison behavior vector sequence to obtain first target e-commerce behavior data corresponding to the target e-commerce behavior data.
The training convergence assessment value between the target behavior vector sequence and the comparison behavior vector sequence may specifically be a loss function value between the target behavior vector sequence and the comparison behavior vector sequence, and the loss function value may represent a magnitude of the training convergence assessment value. The larger the loss function value, the larger the training convergence evaluation value, and the smaller the loss function value, the smaller the training convergence evaluation value.
In an embodiment, the step of updating the comparison e-commerce behavior data based on the training convergence assessment value between the target behavior vector sequence and the comparison behavior vector sequence to obtain the first target e-commerce behavior data corresponding to the target e-commerce behavior data may include:
updating the comparison e-commerce behavior data based on the training convergence evaluation value between the target behavior vector sequence and the comparison behavior vector sequence to obtain to-be-determined first target e-commerce behavior data;
taking the undetermined first target e-commerce behavior data as new comparison e-commerce behavior data;
carrying out vector mining on the new comparison e-commerce behavior data through the initially trained first vector mining unit to obtain a comparison behavior vector sequence corresponding to the new comparison e-commerce behavior data;
and returning to execute the training convergence evaluation value based on the target behavior vector sequence and the comparison behavior vector sequence, updating the comparison e-commerce behavior data to obtain the to-be-determined first target e-commerce behavior data, and taking the to-be-determined first target e-commerce behavior data meeting the target updating requirement as the first target e-commerce behavior data corresponding to the target e-commerce behavior data until the to-be-determined first target e-commerce behavior data meets the target updating requirement.
The target updating requirement may specifically be an iteration number, a loss function value between the to-be-determined first target e-commerce behavior data and the target e-commerce behavior data, or a training convergence evaluation value between the target behavior vector sequence and the comparison behavior vector sequence smaller than a preset evaluation value, and the like, and the target updating requirement may be designed according to an actual service requirement, and is not particularly limited herein.
Wherein, through the above-mentioned circulation process: extracting features of comparison e-commerce behavior data, calculating a training convergence evaluation value, updating the comparison e-commerce behavior data, calculating a training convergence evaluation value, and updating the comparison e-commerce behavior data … …, repeating for multiple times, so that the extracted comparison behavior vector sequence is close to the target behavior vector sequence, but the obtained first target e-commerce behavior data apparently only contains the vector sequence learned by the first vector mining unit.
In an embodiment, the step of updating the comparison e-commerce behavior data based on the training convergence assessment value between the target behavior vector sequence and the comparison behavior vector sequence to obtain the first target e-commerce behavior data corresponding to the target e-commerce behavior data may include:
calculating a training convergence evaluation value between the target behavior vector sequence and the comparison behavior vector sequence;
determining a gradient of the training convergence evaluation value to the control e-commerce behavior data;
and updating the comparison e-commerce behavior data based on the gradient to obtain first target e-commerce behavior data corresponding to the target e-commerce behavior data.
Step A150, training an E-commerce intention mining network according to the target E-commerce behavior data and the first target E-commerce behavior data to obtain the trained E-commerce intention mining network, wherein the trained E-commerce intention mining network is used for intention mining of E-commerce behavior data.
In an embodiment, the step of training the e-commerce intention mining network according to the target e-commerce behavior data and the first target e-commerce behavior data to obtain a trained e-commerce intention mining network may include:
respectively determining the target e-commerce behavior data and the first target e-commerce behavior data as selected e-commerce behavior data;
vector mining is carried out on the selected e-commerce behavior data through the first vector mining unit and the second vector mining unit after the initial training, and a behavior vector sequence of the selected e-commerce behavior data is obtained;
performing vector recovery processing on the behavior vector sequence of the selected e-commerce behavior data through the vector recovery unit to obtain the selected e-commerce behavior data after vector recovery;
optimizing unit importance coefficient information in a second vector mining unit of the E-commerce intention mining network based on the first training convergence evaluation value between the selected E-commerce behavior data after vector recovery and the selected E-commerce behavior data to obtain the trained E-commerce intention mining network.
The behavior vector sequence of the selected e-commerce behavior data may include a first behavior vector sequence and a second behavior vector sequence, for example, the first vector mining unit after the initial training may perform vector mining on the selected e-commerce behavior data to obtain the first behavior vector sequence of the selected e-commerce behavior data; and carrying out vector mining on the selected e-commerce behavior data through a second vector mining unit to obtain a second behavior vector sequence of the selected e-commerce behavior data.
The vector recovery processing is performed on the behavior vector sequence of the selected e-commerce behavior data, that is, the selected e-commerce behavior data is reconstructed by the vector recovery unit based on the behavior vector sequence of the selected e-commerce behavior data, so that the reconstructed selected e-commerce behavior data (the selected e-commerce behavior data after vector recovery) is close to the original selected e-commerce behavior data.
The unit importance coefficient information in the second vector mining unit in the e-commerce intention mining network is optimized based on the first training convergence evaluation value, specifically, the unit weight information in the second vector mining unit is optimized through a back propagation algorithm, so that the first training convergence evaluation value is smaller than a preset training convergence evaluation value, and the preset training convergence evaluation value may be set according to an actual situation, which is not limited in this embodiment.
The training process of the e-commerce intention mining network can be that target e-commerce behavior data and first target e-commerce behavior data are used as selected e-commerce behavior data and are respectively input into the e-commerce intention mining network, a behavior vector sequence corresponding to the selected e-commerce behavior data is obtained through extraction of a first vector mining unit and a second vector mining unit after initial training, vector recovery is carried out through a vector recovery unit based on the behavior vector sequence, the selected e-commerce behavior data is reconstructed, and the selected e-commerce behavior data after vector recovery is obtained.
In the process of training the e-commerce intention mining network, optimizing unit weight information in a second vector mining unit based on a first training convergence evaluation value to distinguish vectors of the target e-commerce behavior data and the first target e-commerce behavior data, so that extraction of depth-related vectors can be achieved, and the second vector mining unit also contains other depth-related vectors.
In an independently implementable embodiment, the e-commerce intention mining network may further include a first intention mining unit and a second intention mining unit; the behavior vector sequence of the selected e-commerce behavior data comprises a first behavior vector sequence and a second behavior vector sequence of the target e-commerce behavior data, the first behavior vector sequence is obtained by extraction of the first vector mining unit after the initial training, and the second behavior vector sequence is obtained by extraction of the second vector mining unit; the target e-commerce intention information is a prediction confidence coefficient that target e-commerce behavior data belongs to candidate e-commerce intentions;
the step of optimizing unit importance coefficient information in a second vector mining unit of the e-commerce intention mining network based on a first training convergence evaluation value between the selected e-commerce behavior data after the vector recovery and the selected e-commerce behavior data to obtain the trained e-commerce intention mining network may include:
performing intention mining on the target e-commerce behavior data based on a first behavior vector sequence of the target e-commerce behavior data through the first intention mining unit to obtain an actual first prediction confidence coefficient that the target e-commerce behavior data belongs to a candidate e-commerce intention;
optimizing unit importance coefficient information of the initially trained first vector mining unit and the first intention mining unit based on the actual first prediction confidence and a second training convergence evaluation value between the prediction confidence;
performing intention mining on the target e-commerce behavior data based on a second behavior vector sequence of the target e-commerce behavior data through the second intention mining unit to obtain an actual second prediction confidence coefficient that the target e-commerce behavior data belongs to a candidate e-commerce intention;
optimizing unit importance coefficient information of the second intention mining unit based on the actual second prediction confidence and a third training convergence evaluation value between the prediction confidence;
optimizing unit importance coefficient information in the second vector mining unit based on a first training convergence evaluation value between the selected E-commerce behavior data after the vector recovery and the selected E-commerce behavior data;
and terminating the optimization process when the first training convergence evaluation value, the second training convergence evaluation value and the third training convergence evaluation value are converged to obtain the trained E-business intention mining network.
The second intention mining unit is used as an output node of the second vector mining unit, the second behavior vector sequence extracted by the second vector mining unit is used as an input, and the intention mining is performed on the target e-commerce behavior data based on the second behavior vector sequence to obtain an actual second prediction confidence that the target e-commerce behavior data belongs to the candidate e-commerce intention. The second intention mining unit may specifically be a recurrent neural network or a fully connected deep neural network or the like.
The unit importance coefficient information of the second intention mining unit is optimized based on the third training convergence evaluation value, specifically, the unit weight information of the second intention mining unit is optimized through a back propagation algorithm based on the third training convergence evaluation value, so that the actual second prediction confidence coefficient of the target e-commerce behavior data belonging to the candidate e-commerce intention approaches to the prediction confidence coefficient.
The first behavior vector sequence and the second behavior vector sequence are vector sequences with deep association, for example, the first behavior vector sequence and the second behavior vector sequence are vector sequences related to target e-commerce intention information of target e-commerce behavior data, and can be used in intention mining of the target e-commerce behavior data. For example, in an independently implementable embodiment, the first behavior vector sequence may be a vector sequence strongly correlated with the target e-commerce intention information of the target e-commerce behavior data (i.e., an easily learned vector sequence), and the second behavior vector sequence may be a vector sequence weakly correlated with the target e-commerce intention information of the target e-commerce behavior data.
Wherein the unit weight information of the first vector mining unit can be denoted as ma, the unit weight information of the first intent mining unit can be denoted as wc, the unit weight information of the second vector mining unit can be denoted as fs, and the unit weight information of the second intent mining unit can be denoted as ws. The target e-commerce behavior data can be processed through the vector compression unit, and the target e-commerce behavior data processed by the vector compression unit are respectively input to the first vector mining subunit and the second vector mining subunit for vector mining to obtain a first behavior vector sequence and a second behavior vector sequence; and performing intention mining on the first behavior vector sequence through the first classification model, and performing intention mining on the second behavior vector sequence through the second intention mining unit.
In an embodiment, the step of terminating the optimization process when the first training convergence evaluation value, the second training convergence evaluation value, and the third training convergence evaluation value converge to obtain a trained e-commerce intention mining network may include:
determining importance coefficients corresponding to the first training convergence evaluation value, the second training convergence evaluation value and the third training convergence evaluation value;
based on the importance coefficient, performing importance coefficient fusion on the first training convergence evaluation value, the second training convergence evaluation value and the third training convergence evaluation value to obtain a global training convergence evaluation value of the E-commerce intention mining network;
and terminating the optimization process when the global training convergence evaluation value is converged to obtain the trained E-business intention mining network.
After the e-commerce intent mining network is trained, the e-commerce intent mining network may be applied to online e-commerce behavioral data intent mining, as described below.
In an independently implementable embodiment, the disclosed embodiment provides an e-commerce behavior data intent mining method based on artificial intelligence, which may include the following steps:
acquiring target e-commerce behavior data to be subjected to intention mining;
vector mining is carried out on the target e-commerce behavior data through a first vector mining unit in the trained e-commerce intention mining network to obtain a first behavior vector sequence of the target e-commerce behavior data, and intention mining is carried out on the target e-commerce behavior data based on the first behavior vector sequence of the target e-commerce behavior data to obtain a first prediction confidence coefficient that the target e-commerce behavior data belongs to candidate e-commerce intentions;
vector mining is carried out on the target e-commerce behavior data through a second vector mining unit in the trained e-commerce intention mining network to obtain a second behavior vector sequence of the target e-commerce behavior data, and intention mining is carried out on the target e-commerce behavior data based on the second behavior vector sequence of the target e-commerce behavior data to obtain a second prediction confidence coefficient that the target e-commerce behavior data belongs to candidate e-commerce intentions;
determining a target mining intent sequence of the target e-commerce behavioral data based on the first prediction confidence and the second prediction confidence.
The target e-commerce behavior data can be subjected to intent mining by the trained first intent mining unit based on the first behavior vector sequence of the target e-commerce behavior data, for example, the first behavior vector sequence is obtained by the first vector mining unit, and the first behavior vector sequence can be multiplied by the unit weight information wc in the first intent mining unit to represent intent mining information.
The trained second intention mining unit can perform intention mining on the target e-commerce behavior data based on the second behavior vector sequence of the target e-commerce behavior data, for example, the second vector mining unit obtains the second behavior vector sequence, and the second behavior vector sequence can be multiplied by the unit weight information ws in the second intention mining unit to represent intention mining information.
And determining a target mining intention sequence of the target e-commerce behavior data based on the mean confidence.
In an embodiment, the step of determining the target mining intent sequence of the target e-commerce behavior data based on the first prediction confidence and the second prediction confidence may include:
determining importance coefficients corresponding to the first prediction confidence coefficient and the second prediction confidence coefficient;
and performing importance coefficient fusion on the first prediction confidence coefficient and the second prediction confidence coefficient based on the importance coefficient to obtain a target mining intention sequence of the target e-commerce behavior data.
According to the method, the comparison e-commerce behavior data can be updated through the first vector mining unit after initial training based on the target e-commerce behavior data to obtain the first target e-commerce behavior data corresponding to the target e-commerce behavior data, and then the e-commerce intention mining network is trained based on the target e-commerce behavior data and the first target e-commerce behavior data to extract the deep association vector. The extraction of the depth association vectors is beneficial to the intention mining of the e-commerce behavior data according to the plurality of depth association vectors when the e-commerce behavior data are intended to be mined, the problem that only a certain simple vector is concerned and other depth association vectors are ignored is avoided, and the accuracy of the network for the e-commerce behavior data mining is improved.
In an embodiment that can be implemented independently, the embodiment of the present disclosure further provides an information display method based on e-commerce product push and big data, including the following steps:
step B110: and acquiring corresponding information of the e-commerce product to be pushed and content pushing decision information of the e-commerce product to be pushed based on the target mining intention sequence of the target e-commerce behavior data, wherein the content pushing decision information comprises one or more combinations of pushing attribute information of the e-commerce product to be pushed, a related user portrait of the information of the e-commerce product to be pushed or related pushing reference information of the e-commerce product to be pushed.
In an embodiment, the content push decision information refers to information having direct characteristic contact information with the push characteristics of the push content information of the to-be-pushed e-commerce product information, and includes one or more combinations of push attribute information of the to-be-pushed e-commerce product information, a related user portrait of the to-be-pushed e-commerce product information, or related push reference information of the to-be-pushed e-commerce product information.
Wherein, one push content can simultaneously have one or more pieces of push attribute information. The push attribute information may represent a push content information type of the push content or some specific information in the push content, for example, if the push attribute information of a certain push content is a digital product, the push content belongs to push content information of the digital product type. In a possible design idea, the pushing attribute information may be pre-labeled or automatically identified by the pushing content, for example, by extracting key information in the information of the e-commerce product to be pushed, automatically adding the pushing attribute information to the information of the e-commerce product to be pushed based on the key information, or classifying the information of the e-commerce product to be pushed, and determining the pushing attribute information of the pushing content based on the classification.
In a separately implementable embodiment, the push attribute information may be displayed in the form of letters or numbers in the subject of the push content.
The related user portrait refers to a user portrait related to the to-be-pushed e-commerce product information, for example, a user portrait referring to the to-be-pushed e-commerce product information, a user portrait sharing the to-be-pushed e-commerce product information, and the like, and the pushed content information characteristics of the to-be-pushed e-commerce product information can be reflected from the other side through the related user portrait.
In an independently implementable embodiment, the associated user representation comprises one or more combinations of a target active user representation of the e-commerce product information to be pushed or a passive user representation of the e-commerce product information to be pushed; the related push reference information comprises target push content decision information which is referred by a target active user portrait of the to-be-pushed e-commerce product information, and the target push content decision information is at least one piece of push content decision information of the target active user portrait before and/or after the target active user portrait refers to the to-be-pushed e-commerce product information.
The related push reference information is at least one piece of push content decision information which is referred by the target active user portrait before and/or after the E-commerce product information to be pushed is referred, and the push content information preference of the user can be reflected through the related push reference information.
In an independently implementable embodiment, obtaining a target active user representation of an e-commerce product information to be pushed comprises:
acquiring initial active user figures of the E-commerce product information to be pushed and reference continuous data of the initial active user figures of the E-commerce product information to be pushed;
and based on the citation continuous data corresponding to each active user portrait, taking the initial active user portrait covered in the first pushing stage in the citation continuous data of each initial active user portrait as a target active user portrait, or based on the citation continuous data corresponding to each active user portrait, taking the initial active user portrait of which the citation continuous data in each initial active user portrait meets a preset coverage range as the target active user portrait.
The initial active user portrait refers to all user portraits which quote the information of the E-commerce products to be pushed, the quote continuous data refers to quote track information corresponding to the information of the E-commerce products to be pushed at one time, and the quote continuous data is not covered on the integral push track information of the E-commerce products to be pushed. The coverage range of the continuous data for quoting the to-be-pushed e-commerce product information is large in rain, and the active user image has preference for the to-be-pushed e-commerce product information. And selecting the quoting continuous data to be covered on the active user portrait in the first pushing stage, so that the characteristics of the user portrait quoting the to-be-pushed e-commerce product information can be more accurately reflected.
In an embodiment, the related push reference information may be obtained by at least one of:
the method comprises the steps of sequencing pushed content information quoted by target active user images according to quote continuous data, and combining one or more pieces of first preset quantity of pushed content information before or second preset quantity of pushed content information after the information of the E-commerce products to be pushed in each sequenced pushed content to serve as related pushed quote information of the E-commerce product information to be pushed.
And sequencing the pushed content information quoted by the target active user image according to the quoted sequence, and combining one or more pieces of the third preset quantity of pushed content information before or the fourth preset quantity of pushed content information after the to-be-pushed e-commerce product information in each sequenced pushed content as related pushed quote information of the to-be-pushed e-commerce product information.
And taking the push content information quoted by the target active user portrait of the e-commerce product information to be pushed in a second push stage as related push quote information, wherein the second push stage is a push stage of quote continuous data relative to the e-commerce product information to be pushed.
And step B120, acquiring the information of the E-commerce product to be pushed and the initial push decision characteristics of each decision information in the content push decision information.
The initial push decision characteristics of the to-be-pushed e-commerce product information can reflect the characteristics of the to-be-pushed e-commerce product information, and the initial push decision characteristics of each piece of decision information in the content push decision information can reflect the characteristics of each piece of decision information.
In an embodiment that can be implemented independently, obtaining an initial push decision feature of each piece of decision information in the to-be-pushed e-commerce product information and the content push decision information includes:
acquiring an e-commerce pushing theme of the e-commerce product information to be pushed, extracting theme pushing decision characteristics corresponding to the e-commerce pushing theme, and taking the theme pushing decision characteristics as initial pushing decision characteristics of the e-commerce product information to be pushed;
if the content push decision information comprises the related user portrait, for any related user portrait, obtaining the past push content information corresponding to the related user portrait, and determining the initial push decision characteristic of the related user portrait based on the past push content information.
The e-commerce pushing theme can reflect relevant theme characteristics of the pushing content, and then the theme pushing decision characteristics of the e-commerce pushing theme can be used as initial pushing decision characteristics of the knowledge entity corresponding to the e-commerce product information to be pushed.
For example, in an embodiment that can be implemented independently, the theme push decision feature corresponding to the extracted e-commerce push theme can be implemented by:
splitting the topic of the e-commerce push topic to obtain each split sub topic contained in the e-commerce push topic; extracting sub-pushing decision characteristics of each split sub-topic; determining topic push decision characteristics based on the sub-push decision characteristics of each split sub-topic. In an embodiment that can be implemented independently, sub-push decision features of each split sub-topic can be fused in a weighted manner to obtain topic push decision features.
In an embodiment, the target push decision characteristics of each past push content message may be weighted and averaged, and the weighted and averaged result may be used as the initial push decision characteristic of the knowledge entity corresponding to the target active user representation or passive user representation.
In an independently implementable embodiment, the related user representation includes one or more combinations of a target active user representation of the to-be-pushed e-commerce product information or a passive user representation of the to-be-pushed e-commerce product information, if the related user representation includes the target active user representation, the past pushed content information is pushed content information referenced by the target active user representation in a first pushing stage before a current pushing node, and if the related user representation includes the passive user representation, the past pushed content information is pushed content information referenced by the passive user representation in a second pushing stage before the current pushing node.
In an embodiment, if the relevant user profile comprises a target active user profile, a recent (less time-spaced from the reference time sequence for referencing the information of the electronic product to be pushed) referenced push content information may be selected as the past push content information from at least one push content decision information referenced in a first push stage prior to the current push node. The recently quoted push content information can reflect the preference change of the user more accurately, so that the determined initial push decision characteristic of the knowledge entity corresponding to the target active user portrait is more accurate.
In an embodiment, if the associated user profile comprises a passive user profile, a recent (less time interval from the reference of the to-be-pushed e-commerce product information) referenced push content information may be selected as the past push content information from the at least one push content decision information referenced in the second push stage prior to the current push node. The recently quoted push content information can reflect the preference change of the user more accurately, so that the determined initial push decision characteristic of the knowledge entity corresponding to the passive user portrait is more accurate.
Step B130: and determining characteristic contact information corresponding to the information of the E-commerce product to be pushed, wherein the characteristic contact information is characteristic contact information between the information of the E-commerce product to be pushed and each decision information in the content pushing decision information.
Step B140: and determining target push decision characteristics of the e-commerce product information to be pushed based on the initial push decision characteristics and the characteristic contact information, so as to carry out push decision of the e-commerce product information to be pushed based on the target push decision characteristics.
The characteristic contact information can reflect the characteristic contact information between the E-commerce product information to be pushed and each piece of decision information in the content pushing decision information, each initial pushing decision characteristic can reflect the self characteristic of the pushing content information of the E-commerce product information to be pushed and the self characteristic of each piece of decision information in the content pushing decision information, and therefore the pushing decision processing of the pushing content by the target pushing decision characteristic determined based on each initial pushing decision characteristic and the characteristic contact information is more accurate.
Based on the above steps, when obtaining the push decision characteristics of the push content, in addition to considering the information of the e-commerce product to be pushed, the content push decision information of the push content is also considered, specifically, the content push decision information includes one or more combinations of push attribute information of the e-commerce product to be pushed, a related user portrait of the e-commerce product to be pushed, or related push reference information of the e-commerce product to be pushed, the push attribute information is a characteristic capable of reflecting the category of the push content itself, the related push reference information can reflect the push content information preference of the active user portrait, that is, the related push reference information can reflect the push content information characteristics of the e-commerce product to be pushed from other dimensions, and the e-commerce product to be pushed is usually related to the matching preference of the related user portrait, so the related user portrait is also related information capable of reflecting the push content characteristics, therefore, the determined target push decision-making characteristic not only contains the information of the push content, but also contains a plurality of pieces of information with different dimensions related to the push content, so that the target push decision-making characteristic can more accurately process the push decision of the push content.
In an embodiment that can be implemented independently, determining feature contact information corresponding to information of an e-commerce product to be pushed includes:
constructing a knowledge network corresponding to the information of the e-commerce product to be pushed based on the content pushing decision information and the information of the e-commerce product to be pushed, wherein the knowledge network represents characteristic contact information;
the knowledge entities in the knowledge network comprise knowledge entities corresponding to-be-pushed e-commerce product information and knowledge entities corresponding to each piece of decision information in the content pushing decision information, and the connection line in the knowledge network comprises entity contact attributes between the to-be-pushed e-commerce product information and the knowledge entities corresponding to each piece of decision information in the content pushing decision information;
determining target push decision characteristics of the e-commerce product information to be pushed based on each initial push decision characteristic and the characteristic contact information, wherein the target push decision characteristics comprise:
and determining target push decision characteristics of the E-commerce product information to be pushed based on the initial push decision characteristics and the knowledge network.
And the characteristic contact information between the content pushing decision information and the information of the E-commerce products to be pushed is represented through a knowledge network. The knowledge network not only comprises information of all knowledge entities, but also comprises information of all connecting lines in the knowledge network, namely characteristic contact information, and the characteristic contact information is represented based on the knowledge network, so that the characteristics corresponding to the information of the e-commerce products to be pushed can be more accurately reflected.
In an embodiment, the content push decision information includes at least one push attribute information, and the connection in the knowledge network further includes an entity contact attribute between the knowledge entities corresponding to the push attribute information.
Wherein, if the content push decision information comprises at least two push attribute information, namely the information of the e-commerce product to be pushed has at least two push categories, since the plurality of pushing attribute information are all the categories of the to-be-pushed e-commerce product information and are provided with the characteristic contact information, therefore, the connection in the knowledge network may further include an entity contact attribute between the knowledge entities corresponding to each piece of push attribute information, so as to indicate that the knowledge entities connected by the entity contact attribute all correspond to the category of the processing push content through the entity contact attribute, therefore, the knowledge network more accurately and thinly expresses the characteristic contact information between the information of the e-commerce product to be pushed and each piece of pushing attribute information of the e-commerce product to be pushed, and more accurate characteristic expression of the information of the e-commerce product to be pushed, namely target pushing decision characteristics, can be obtained based on the knowledge network architecture.
In an independently implementable embodiment, determining a target push decision feature for the e-commerce product information to be pushed based on each initial push decision feature and the knowledge network comprises:
for a target knowledge entity in a knowledge network, extracting and obtaining a first push decision characteristic corresponding to label description of each push decision label based on an initial push decision characteristic corresponding to each contact knowledge entity of each push decision label of the target knowledge entity, wherein the target knowledge entity is a knowledge entity corresponding to-be-pushed e-commerce product information, and the knowledge entity corresponding to each item of push decision information in content push decision information belongs to a knowledge entity of a push decision label;
and extracting target pushing decision characteristics of the to-be-pushed e-commerce product information based on the first pushing decision characteristics corresponding to the target knowledge entity and the initial pushing decision characteristics of the target knowledge entity.
The contact knowledge entity of the target knowledge entity refers to a knowledge entity having entity contact attribute with the target knowledge entity, and the contact knowledge entity can reflect certain characteristics of the target knowledge entity.
The contact knowledge entities of different push decision tags correspondingly reflect different knowledge entity characteristics, so that when the entity push decision characteristics (first push decision characteristics) of each contact knowledge entity are extracted, the extraction can be carried out according to the push decision tags of the contact knowledge entities. The label descriptions of the same push decision label correspond to a first push decision feature.
It should be noted that, for each knowledge entity in the knowledge network, an initial push decision feature corresponding to the label description of each push decision label of the knowledge entity needs to be extracted to obtain a first push decision feature corresponding to the label description of each push decision label corresponding to the knowledge entity.
In a separate embodiment, the method may further comprise the steps of:
for each knowledge entity in the knowledge network, extracting entity push decision characteristics of the knowledge entity by executing at least one of the following processes:
extracting to obtain a second push decision characteristic corresponding to the label description of the push decision label based on the current push decision characteristic of each contact knowledge entity of each push decision label of the knowledge entity; obtaining a target pushing decision characteristic of the knowledge entity based on the current pushing decision characteristic of the knowledge entity and each second pushing decision characteristic corresponding to the knowledge entity; if the flow is once, the current push decision characteristic is an initial push decision characteristic, the target push decision characteristic is a knowledge entity push decision characteristic, if the flow is at least twice, the current push decision characteristic corresponding to the first flow is the initial push decision characteristic, except that the current push decision characteristic corresponding to the first flow before the first flow is made is a target push decision characteristic obtained by the last flow, the knowledge entity push decision characteristic is a target push decision characteristic obtained by the last flow;
the method comprises the steps of carrying out further feature extraction on each knowledge entity based on initial push decision features of each knowledge entity in a knowledge network to obtain entity push decision features of each knowledge entity, and pushing the features of the decision features to represent the knowledge entities more deeply through the knowledge entities.
After obtaining the initial push decision feature of the knowledge entity, for each knowledge entity in the knowledge network, at least one feature extraction may be performed on the knowledge entity based on the initial push decision feature of the knowledge entity to obtain the entity push decision feature of the knowledge entity, that is, one feature extraction is performed corresponding to one process. And the target push decision characteristic obtained by the current flow is used as the current push decision characteristic of the next flow.
Based on the initial push decision feature corresponding to each contact knowledge entity of each push decision tag of the target knowledge entity, extracting and obtaining a first push decision feature corresponding to the tag description of each push decision tag, wherein the first push decision feature comprises the following steps:
for each push decision label, fusing entity push decision characteristics of each contact knowledge entity of the push decision label of the target knowledge entity to obtain a first push decision characteristic corresponding to the label description of the push decision label.
Based on each first push decision characteristic corresponding to the target knowledge entity and the initial push decision characteristic of the target knowledge entity, extracting a target push decision characteristic of the to-be-pushed e-commerce product information, comprising:
splicing each first pushing decision characteristic corresponding to the target knowledge entity with the entity pushing decision characteristic of the target knowledge entity;
and extracting target push decision characteristics of the E-commerce product information to be pushed based on the spliced push decision characteristics.
The entity push decision characteristics of different contact knowledge entities reflect different characteristics corresponding to each contact knowledge entity of any push decision label, the entity push decision characteristics of each contact knowledge entity are fused, and the fused push decision characteristics are used as the first push decision characteristics corresponding to the label description of the push decision label. And carrying out the same pushing decision on each contact knowledge entity of each pushing decision label of the target knowledge entity in the knowledge network to obtain a first pushing decision characteristic corresponding to the label description of each pushing decision label of the target knowledge entity.
After the first push decision characteristics corresponding to the label descriptions of the various push decision labels are obtained, the first push decision characteristics corresponding to the label descriptions of the various push decision labels and the entity push decision characteristics of the target knowledge entity can be spliced to obtain spliced push decision characteristics, the spliced push decision characteristics comprise the push decision characteristics of the contact knowledge entities and the push decision characteristics of the target knowledge entity, and the target push decision characteristics of the to-be-pushed e-commerce product information obtained by further characteristic extraction of the spliced push decision characteristics are more accurate.
In an embodiment that can be implemented independently, concatenating a first push decision feature corresponding to a tag description of each push decision tag and an entity push decision feature of a target knowledge entity includes:
acquiring a first importance coefficient corresponding to the label description of each pushing decision label and a second importance coefficient corresponding to the information of the E-commerce product to be pushed;
weighting a first pushing decision characteristic corresponding to the label description of each pushing decision label based on a first importance coefficient corresponding to the label description of each pushing decision label to obtain a second pushing decision characteristic corresponding to the label description of each pushing decision label;
weighting the entity pushing decision characteristics of the target knowledge entity based on the second importance coefficient to obtain third pushing decision characteristics; and splicing the second pushing decision characteristic and the third pushing decision characteristic corresponding to the label description of each pushing decision label.
Because the importance degrees of the information with different dimensions on the target pushing decision characteristics of the to-be-pushed e-commerce product information are different, the first importance coefficients corresponding to the label descriptions of the various pushing decision labels and the second importance coefficients corresponding to the to-be-pushed e-commerce product information can be spliced on the basis of the first importance coefficients corresponding to the label descriptions of the various pushing decision labels and the entity pushing decision characteristics of the target knowledge entity, so that the influence of the information with different dimensions on the target pushing decision characteristics is fully considered in the obtained spliced pushing decision characteristics, and the finally determined target pushing decision characteristics are more accurate.
In an independently implementable embodiment, the first importance coefficient may be a matrix arrangement, and for a target knowledge entity, each element in the matrix arrangement corresponding to the target knowledge entity corresponds to a tag description of each push decision tag of the target knowledge entity corresponding to a first push decision feature.
In an independently implementable embodiment, the first importance coefficients corresponding to the tag descriptions of push decision tags for different knowledge entities in the knowledge network may be different.
After the target push decision characteristic of the e-commerce product information to be pushed is obtained, a push decision can be made on the e-commerce product information to be pushed based on the target push decision characteristic.
In an embodiment that can be implemented independently, the to-be-pushed e-commerce product information is push content information referred by a user, and a push decision of the to-be-pushed e-commerce product information is made based on a target push decision characteristic, including:
determining target push content from a first push content source based on a target push decision characteristic of the E-commerce product information to be pushed and a correlation metric value of a target push decision characteristic of each candidate push content in the first push content source, and sending the target push content to the E-commerce using terminal, wherein the E-commerce product information to be pushed is push content information quoted by the E-commerce using terminal; alternatively, the first and second electrodes may be,
and performing clustering arrangement on all the pushed contents in the second pushed content source based on the characteristic distance between the target pushing decision characteristics of all the pushed contents in the second pushed content source, wherein the to-be-pushed e-commerce product information is each pushed content in the second pushed content source.
One implementation scheme for determining candidate push contents associated with the e-commerce product information to be pushed from the first push content source based on the target push decision characteristics of the e-commerce product information to be pushed is as follows: the method comprises the steps of determining target push decision characteristics of all candidate push contents in a first push content source based on a method for determining that the target push decision characteristics of the to-be-pushed e-commerce product information are the same, then determining target push contents related to the to-be-pushed e-commerce product information based on characteristic distances between the target push decision characteristics of the to-be-pushed e-commerce product information and the target push decision characteristics of all candidate push contents, and sending the target push contents to the e-commerce use terminal.
In addition, in an embodiment that can be implemented independently, the push decision of the to-be-pushed e-commerce product information based on the target push decision feature can be further implemented by the following steps.
(1) Acquiring first tendency knowledge graph information and second tendency knowledge graph information which are obtained after tendency analysis is carried out on the target pushing decision characteristics, wherein the first tendency knowledge graph information is dynamic tendency knowledge graph information of active content updating service, and the second tendency knowledge graph information is static tendency knowledge graph information including passive content updating service;
(2) determining relevant information of corresponding knowledge graph content nodes in the first tendency knowledge graph information and the second tendency knowledge graph information, and determining a target knowledge graph content node which is corresponding to the first tendency knowledge graph information and the second tendency knowledge graph information and meets a preset subscription pushing condition based on the relevant information of the corresponding knowledge graph content nodes;
(3) performing content update synchronization verification on the target knowledge-graph content node in the second tendency knowledge-graph information based on the target knowledge-graph content node in the first tendency knowledge-graph information;
(4) integrating content node information in the second tendency knowledge graph information after synchronous verification of content updating to obtain target tendency knowledge graph information, and generating an information pushing strategy aiming at the to-be-pushed e-commerce product information according to the first tendency knowledge graph information and the target tendency knowledge graph information.
In an independently implementable embodiment, determining, based on each initial push decision feature and the feature contact information, that a target push decision feature of the e-commerce product information to be pushed is implemented by an AI training network, the training step of the AI training network includes:
acquiring a training basic data set, wherein the training basic data set comprises a plurality of training basic data, each training basic data comprises a reference knowledge network corresponding to reference push content and initial push decision characteristics of knowledge entities in the reference knowledge network, each knowledge entity in any reference knowledge network comprises a first knowledge entity corresponding to the reference push content and a second knowledge entity corresponding to each first push decision information, the first push decision information is any push decision information in the content push decision information of the reference push content, and a connecting line in the reference knowledge network comprises entity contact attributes between the first knowledge entity and each second knowledge entity;
inputting each training basic data into an initial AI training network to obtain the prediction push decision-making characteristics of each knowledge entity corresponding to each training basic data;
for each training basic data, determining a first convergence evaluation index value corresponding to the training basic data based on the characteristic distance between the prediction pushing decision characteristic of the first knowledge entity and the prediction pushing decision characteristic of each second knowledge entity in the reference knowledge network of the training basic data;
determining a total convergence evaluation index value corresponding to the AI training network based on the first convergence evaluation index values corresponding to the training basic data;
if the total convergence evaluation index value meets the convergence requirement, terminating the training process, and taking the finally output target network as an AI training network, otherwise, adjusting the network weight configuration information of the AI training network, and continuing training the AI training network based on the training basic data set.
The reference knowledge network refers to a knowledge network corresponding to reference push content, and the initial push decision characteristics of the knowledge entities in the reference knowledge network can be determined based on the described manner of the initial push decision characteristics of the knowledge entities in the knowledge network corresponding to the information of the e-commerce product to be pushed.
Fig. 3 is a schematic diagram illustrating a hardware structure of an AI pushing system 100 for implementing the above-mentioned internet e-commerce big data based information pushing method according to an embodiment of the present application, where, as shown in fig. 3, the AI pushing system 100 may include a processing chip 110 and a machine-readable storage medium 120; the machine-readable storage medium 120 has stored thereon executable code, which, when executed by the processing chip 110, causes the processing chip 110 to perform the steps of the above embodiment of the information pushing method based on internet e-commerce big data.
In practice, the AI pushing system may further include a communication interface 140, the processing chip 110, the machine-readable storage medium 120 and the communication interface 140 are connected via the bus 130, and the communication interface 140 is used for communicating with other devices.
In addition, the present application provides a non-transitory machine-readable storage medium, on which executable code is stored, and when the executable code is executed by the processing chip 110 of the AI pushing system 100, the processing chip may implement at least the steps of the foregoing embodiment of the information pushing method based on internet e-commerce big data.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1. An information pushing method based on internet e-commerce big data is characterized by being applied to an AI pushing system, wherein the AI pushing system is in communication connection with a plurality of e-commerce use terminals, and the method comprises the following steps:
acquiring a target mining intention sequence of the target e-commerce behavior big data of the e-commerce using terminal, and displaying information on the e-commerce using terminal according to the target mining intention sequence;
acquiring a content session activity event set of a pushed content page flow shown by the information by the electronic commerce use terminal, wherein the content session activity event set comprises a plurality of content session activity events;
predicting potential interest activity processes included in each content session activity event and process triggering information corresponding to each potential interest activity process according to a pre-trained potential interest activity prediction model;
and generating information pushing optimization information according to the potential interest activity flow included by each content session activity event and the flow trigger information corresponding to each potential interest activity flow, and optimizing the current information pushing strategy of the electronic commerce use terminal based on the information pushing optimization information.
2. The method for pushing information based on internet e-commerce big data according to claim 1, wherein the step of predicting the potential interest activity flow included in each content session activity event and the flow trigger information corresponding to each potential interest activity flow according to a pre-trained potential interest activity prediction model comprises:
extracting content session activity characteristics corresponding to each content session activity event according to a pre-trained potential interest activity prediction model, and predicting potential interest prediction information corresponding to the content session activity events based on the content session activity characteristics, wherein the potential interest prediction information comprises a prediction confidence coefficient of the content session activity events for each candidate potential interest activity process;
acquiring potential interest activity processes included by each content session activity event based on potential interest prediction information corresponding to the content session activity events;
and acquiring process trigger information corresponding to the potential interest activity processes included in each content session activity event.
3. The information pushing method based on the internet e-commerce big data as claimed in claim 1, further comprising:
acquiring a reference content session activity event set, wherein the reference content session activity event set comprises a plurality of reference content session activity events and reference potential interest information corresponding to each reference content session activity event;
inputting each reference content session activity event in the reference content session activity event set into an initial potential interest activity prediction model to obtain potential interest prediction information corresponding to each reference content session activity event;
optimizing a model weight parameter of the initial potential interest activity prediction model based on the reference potential interest information and the potential interest prediction information, judging whether the initial potential interest activity prediction model meets a model convergence requirement, outputting the potential interest activity prediction model obtained by training when the initial potential interest activity prediction model meets the model convergence requirement, and returning to execute the step of inputting each reference content session activity event in the reference content session activity event set into the initial potential interest activity prediction model to obtain the potential interest prediction information corresponding to each reference content session activity event when the initial potential interest activity prediction model does not meet the model convergence requirement.
4. The method for pushing information based on internet e-commerce big data according to any one of claims 1 to 3, wherein the step of generating information pushing optimization information according to the potential interest activity flow included in each content session activity event and the flow trigger information corresponding to each potential interest activity flow, and optimizing the current information pushing policy of the e-commerce use terminal based on the information pushing optimization information includes:
determining a potential interest portrait sequence of each content session activity event according to process log information of a potential interest activity process included in each content session activity event;
determining a flow trigger portrait sequence of each content session activity event according to flow trigger information corresponding to a potential interest activity flow included in each content session activity event;
fusing the flow triggering portrait sequence with the potential interest portrait sequence to obtain a fused potential interest portrait sequence;
and generating information pushing optimization information based on the fusion potential interest portrait sequence, and optimizing the current information pushing strategy of the electronic commerce use terminal based on the information pushing optimization information.
5. The method as claimed in claim 4, wherein the step of generating information push optimization information based on the fused sequence of potential interest images and optimizing the current information push strategy of the e-commerce usage terminal based on the information push optimization information comprises:
acquiring content hotspot distribution information corresponding to the fusion potential interest portrait sequence, and acquiring the distribution heat value of each content hotspot in the content hotspot distribution information;
summarizing a key content hotspot distribution sequence from the content hotspot distribution information according to the distributed heat value of each content hotspot;
determining candidate content hotspot distribution in the content hotspot distribution information and hotspot tracking cost values of the candidate content hotspot distribution based on the key content hotspot distribution sequence;
obtaining a push content hotspot distribution set in the content hotspot distribution information according to content hotspot distributions except the candidate content hotspot distribution in the content hotspot distribution information and hotspot tracking relations among the content hotspot distributions;
determining hotspot tracking cost values of the distribution of each content hotspot in the push content hotspot distribution set based on the push content hotspot distribution set and the candidate content hotspot distribution; and the determined hotspot tracking cost value is used for generating information push optimization information corresponding to the distribution of the corresponding content hotspots.
6. The information pushing method based on internet e-commerce big data of claim 5, wherein the obtaining of the distribution heat value of each content hotspot in the content hotspot distribution information comprises:
acquiring the content hotspot distribution information;
determining the content hotspot relevancy of the associated content hotspot distribution of each content hotspot distribution in the content hotspot distribution information;
and taking the content hotspot relevance of the distribution of the associated content hotspots as the heat value of the distribution of the corresponding content hotspots.
7. The method according to claim 5, wherein summarizing a key content hotspot distribution sequence from the content hotspot distribution information according to the distribution heat value of each content hotspot comprises:
acquiring a preset target heat value;
and removing the content hotspot distribution with the thermal value smaller than or equal to the target thermal value and the hotspot tracking relation corresponding to the content hotspot distribution from the content hotspot distribution information, and obtaining a key content hotspot distribution sequence according to the candidate content hotspot distribution in the content hotspot distribution information and the hotspot tracking relation between the candidate content hotspot distributions.
8. The internet e-commerce big data-based information pushing method according to claim 5, wherein the determining the candidate content hotspot distribution and the hotspot tracking cost value of the candidate content hotspot distribution in the content hotspot distribution information based on the key content hotspot distribution sequence comprises:
according to the content hotspot relevancy of the content hotspot distribution in the key content hotspot distribution sequence associated with the content hotspot distribution, obtaining the thermal value of each content hotspot distribution in the key content hotspot distribution sequence, and taking the thermal value in the key content hotspot distribution sequence as the initial current hotspot tracking cost value of the corresponding content hotspot distribution;
executing each content hotspot distribution in the key content hotspot distribution sequence in a traversing manner, and determining a balance heat value corresponding to the content hotspot distribution according to the current hotspot tracking cost value of the content hotspot distribution in the key content hotspot distribution sequence;
removing the content hotspot distribution from the key content hotspot distribution sequence when the trade-off thermal force value is less than or equal to a preset target thermal force value;
when the weighted thermal value is larger than the target thermal value and smaller than the current hotspot tracking cost value of the content hotspot distribution, optimizing the current hotspot tracking cost value of the content hotspot distribution according to the weighted thermal value of the content hotspot distribution until the current hotspot tracking cost value of each content hotspot distribution in the key content hotspot distribution sequence is not optimized in the secondary traversal process, and terminating the traversal;
taking content hotspot distribution in a key content hotspot distribution sequence obtained during the ending traversal as the candidate content hotspot distribution, and taking the current hotspot tracking cost value of the candidate content hotspot distribution during the ending traversal as the hotspot tracking cost value corresponding to the candidate content hotspot distribution;
wherein the method further comprises:
after the current traversal is finished, recording the content hotspot distribution with the optimized current hotspot tracking cost value in the current traversal process;
the recorded content hotspot distribution is used for indicating that the recorded content hotspots are distributed in the associated content hotspot distribution in the key content hotspot distribution sequence when the next traversal starts, and the associated content hotspot distribution is used as the target content hotspot distribution of which the weighted heat value needs to be determined again in the next traversal process;
for each content hotspot distribution in the key content hotspot distribution sequence, determining a weighted heat value corresponding to the content hotspot distribution according to a current hotspot tracking cost value of the content hotspot distribution in the associated content hotspot distribution in the key content hotspot distribution sequence, including:
and for target content hotspot distribution in the key content hotspot distribution sequence, determining a balance heat value corresponding to the target content hotspot distribution according to the current hotspot tracking cost value of the target content hotspot distribution in the associated content hotspot distribution in the key content hotspot distribution sequence.
9. The internet e-commerce big data-based information push method according to any one of claims 1 to 8, wherein the step of obtaining a target mining intention sequence of target e-commerce behavior big data of the e-commerce usage terminal and performing information display on the e-commerce usage terminal according to the target mining intention sequence comprises:
the method comprises the steps of obtaining a target e-commerce behavior data set and an e-commerce intention mining network to be configured in a convergence mode, wherein the target e-commerce behavior data set comprises target e-commerce behavior data, target e-commerce intention information corresponding to the target e-commerce behavior data and reference e-commerce behavior data, the e-commerce intention mining network comprises a first vector mining unit, a second vector mining unit and a vector recovery unit, and the first vector mining unit and the second vector mining unit share a configuration vector compression unit;
performing initial training on the first vector mining unit based on the target e-commerce behavior data and the corresponding target e-commerce intention information to obtain a first vector mining unit after the initial training, wherein the first vector mining unit comprises a vector compression unit and a first vector mining subunit;
performing vector mining on the target e-commerce behavior data and the comparison e-commerce behavior data through the initially trained first vector mining unit to obtain a target behavior vector sequence corresponding to the target e-commerce behavior data and a comparison behavior vector sequence corresponding to the comparison e-commerce behavior data;
updating the comparison e-commerce behavior data based on the training convergence evaluation value between the target behavior vector sequence and the comparison behavior vector sequence to obtain first target e-commerce behavior data corresponding to the target e-commerce behavior data;
training an e-commerce intention mining network according to the target e-commerce behavior data and the first target e-commerce behavior data to obtain a trained e-commerce intention mining network, wherein the trained e-commerce intention mining network is used for intention mining of e-commerce behavior data;
acquiring target e-commerce behavior data to be subjected to intention mining of the e-commerce use terminal;
vector mining is carried out on the target e-commerce behavior data through a first vector mining unit in the trained e-commerce intention mining network to obtain a first behavior vector sequence of the target e-commerce behavior data, and intention mining is carried out on the target e-commerce behavior data based on the first behavior vector sequence of the target e-commerce behavior data to obtain a first prediction confidence coefficient that the target e-commerce behavior data belongs to candidate e-commerce intentions;
vector mining is carried out on the target e-commerce behavior data through a second vector mining unit in the trained e-commerce intention mining network to obtain a second behavior vector sequence of the target e-commerce behavior data, and intention mining is carried out on the target e-commerce behavior data based on the second behavior vector sequence of the target e-commerce behavior data to obtain a second prediction confidence coefficient that the target e-commerce behavior data belongs to candidate e-commerce intentions;
determining importance coefficients corresponding to the first prediction confidence coefficient and the second prediction confidence coefficient;
based on the importance coefficient, performing importance coefficient fusion on the first prediction confidence coefficient and the second prediction confidence coefficient to obtain a target mining intention sequence of the target e-commerce behavior data;
acquiring corresponding to-be-pushed e-commerce product information based on a target mining intention sequence of the target e-commerce behavior data, and acquiring content pushing decision information of the to-be-pushed e-commerce product information, wherein the content pushing decision information comprises one or more combinations of pushing attribute information of the to-be-pushed e-commerce product information, a related user portrait of the to-be-pushed e-commerce product information or related pushing reference information of the to-be-pushed e-commerce product information;
acquiring the information of the E-commerce product to be pushed and the initial pushing decision characteristics of each decision information in the content pushing decision information;
determining characteristic contact information corresponding to the to-be-pushed e-commerce product information, wherein the characteristic contact information is characteristic contact information between the to-be-pushed e-commerce product information and each decision information in the content pushing decision information;
and determining target push decision characteristics of the to-be-pushed e-commerce product information based on the initial push decision characteristics and the characteristic contact information, and displaying the to-be-pushed e-commerce product information on the e-commerce use terminal after carrying out push decision on the to-be-pushed e-commerce product information based on the target push decision characteristics.
10. An AI push system, comprising a machine-readable storage medium, a processing chip; wherein the machine-readable storage medium has stored thereon executable code, which when executed by the processing chip, causes the processing chip to execute the internet e-commerce big data based information pushing method of any one of claims 1 to 9.
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