CN114765624A

CN114765624A - Information recommendation method and device, server and storage medium

Info

Publication number: CN114765624A
Application number: CN202011638439.9A
Authority: CN
Inventors: 金雅然; 曾立群; 翟思楠; 马奕潇
Original assignee: Beijing Dajia Internet Information Technology Co Ltd
Current assignee: Beijing Dajia Internet Information Technology Co Ltd
Priority date: 2020-12-31
Filing date: 2020-12-31
Publication date: 2022-07-19
Anticipated expiration: 2040-12-31
Also published as: CN114765624B

Abstract

The disclosure discloses an information recommendation method, an information recommendation device, a server and a storage medium, and belongs to the technical field of communication. The information recommendation method comprises the following steps: receiving a recommendation request sent by a first account of a login application program, wherein the recommendation request is used for requesting to acquire multimedia information; responding to the recommendation request, determining a target account group corresponding to the first account, wherein the target account group comprises a second account, the similarity between the first account information of the first account and the second account information of the second account is greater than or equal to a preset similarity, and the first similarity is determined by the social relationship information between the first account and the second account; acquiring the category of recommended information corresponding to the state information according to the state information of the target account group, wherein the state information is used for representing and predicting the state of the application program used by the second account; multimedia information corresponding to the category of the recommendation information is recommended to the first account. By adopting the information recommendation method, the information recommendation device, the server and the storage medium, the problem that the current information recommendation is inaccurate is at least solved.

Description

Information recommendation method and device, server and storage medium

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, to an information recommendation method, an information recommendation apparatus, a server, and a storage medium.

Background

With the continuous development of internet technology, electronic devices rely on applications to provide services to users. Some application programs can carry out targeted recommendation service to the account according to the current use condition of the account.

However, because the current information recommendation mode is based on recommendation of relatively fixed account groups such as old account groups, inaccurate information recommendation of new accounts can be caused.

Disclosure of Invention

An object of the embodiments of the present disclosure is to provide an information recommendation method, apparatus, server and storage medium, so as to solve at least the problem of inaccurate information recommendation.

The technical scheme of the disclosure is as follows:

according to a first aspect of embodiments of the present disclosure, there is provided an information recommendation method, which may include:

receiving a recommendation request sent by a first account of a login application program, wherein the recommendation request is used for requesting to acquire multimedia information;

responding to the recommendation request, determining a target account group corresponding to the first account, wherein the target account group comprises a second account, the similarity between the first account information of the first account and the second account information of the second account is greater than or equal to a preset similarity, and the first similarity is determined by the social relationship information between the first account and the second account;

acquiring the category of recommended information corresponding to the state information according to the state information of the target account group, wherein the state information is used for representing and predicting the state of the application program used by the second account;

and recommending the multimedia information corresponding to the recommended information category to the first account.

In one possible embodiment, prior to the step of determining the target account group corresponding to the first account, the method may further comprise:

acquiring account information of a plurality of accounts corresponding to the application program;

grouping the accounts according to the similarity of the account information to obtain a plurality of account groups; wherein,

the plurality of account groups includes a target account group.

In another possible embodiment, the step of determining the target account group corresponding to the first account may include:

matching the first account information with account information of accounts in each account group to obtain the similarity of the first account and each account group;

if the similarity between the first account and the account group is greater than the preset similarity, the account group is determined as a target account group.

In yet another possible embodiment, before the step of obtaining the category of the recommendation information corresponding to the status information according to the status information of the target account group, the method may further include: when the second account comprises a fourth account, and the fourth account is a second account with the frequency of accessing the application programs in the target account group being greater than or equal to the preset frequency, summarizing the historical access information of the target account group according to the access information of each second account accessing the application programs in the target account group;

calculating the similarity of the first behavior information of the first account access application program and the second behavior information of the fourth account access application program to obtain the target similarity of the first account and the target account group;

acquiring proportion information of the target account group, wherein the proportion information is used for representing the proportion of a second account in the target account group in the platform account in a preset time period;

status information of account access applications in the target account group is determined based on the historical access information, the target similarity, and the scale information.

In still another possible embodiment, the step of determining the status information of the account access applications in the target account group based on the historical access information, the target similarity and the ratio information may include:

extracting target historical access information in a preset time period from the historical access information;

acquiring the prediction proportion information of the target account group, wherein the prediction proportion information is used for representing and predicting the proportion of a second account in the target account group in the platform account in a preset time period;

and determining state information according to the target historical access information, the prediction proportion information, the target similarity and the proportion information.

Based on this, the step of determining the state information according to the target historical access information, the predicted access ratio information, the target similarity and the ratio information may include:

Z＝1+1/B1*D*(1-T)*B2

wherein z is status information, B1 is scale information, D is target similarity, T is target historical access information, and B2 is predicted scale information.

In still another possible embodiment, in the step of obtaining the category of the recommendation information corresponding to the status information according to the status information of the target account group, the step may include:

determining a target account group type to which the target account group belongs according to the state information of the target account group;

and acquiring the type of the recommendation information corresponding to the target account group type from the preset association information between the account group type and the recommendation information type.

In still another possible embodiment, the step of determining the target account group category to which the target account group belongs according to the status information of the target account group, which is referred to above, may include:

and under the condition that the state information of the target account group comprises the average duration of the second account access application program in the target account group, if the average duration corresponding to the target account group belongs to the duration range of the account group category, determining that the account group category is the target account group category.

According to a second aspect of embodiments of the present disclosure, there is provided an information recommendation apparatus, which may include:

the receiving module is configured to execute receiving of a recommendation request sent by a first account of a login application program, wherein the recommendation request is used for requesting to acquire multimedia information;

the first determination module is configured to execute determining a target account group corresponding to the first account in response to the recommendation request, wherein the target account group comprises a second account, the similarity between the first account information of the first account and the second account information of the second account is greater than or equal to a preset similarity, and the first similarity is determined by the social relationship information between the first account and the second account;

the first acquisition module is configured to acquire the category of the recommendation information corresponding to the status information according to the status information of the target account group, wherein the status information is used for representing and predicting the status of the application program used by the second account;

a recommending module configured to perform recommending multimedia information corresponding to the category of the recommended information to the first account.

In a possible embodiment, the information recommendation apparatus mentioned above further includes:

a second acquisition module configured to perform acquisition of account information of a plurality of accounts corresponding to the application program;

the processing module is configured to group the accounts according to the similarity of the account information to obtain a plurality of account groups; wherein,

the plurality of account groups includes a target account group.

In another possible embodiment, the first determining module referred to above includes:

the matching module is configured to match the first account information with the account information of the accounts in each account group to obtain the similarity between the first account and each account group;

the second determining module is configured to determine the account group as the target account group if the similarity between the first account and the account group is greater than the preset similarity.

In another possible embodiment, the information recommendation apparatus further includes:

the third acquisition module is configured to perform summarizing historical access information of the target account group according to the access information of each second account in the target account group when the second account comprises a fourth account, and the fourth account is the second account with the frequency of accessing the application programs in the target account group being greater than or equal to the preset frequency;

the calculation module is configured to execute the calculation of the similarity between the first behavior information of the first account access application program and the second behavior information of the fourth account access application program, and obtain the target similarity between the first account and the target account group;

the fourth acquisition module is configured to perform acquisition of proportion information of the target account group, wherein the proportion information is used for representing the proportion of a second account in the target account group in the platform account in a predetermined time period;

a third determination module configured to perform determining status information of the account access applications in the target account group based on the historical access information, the target similarity, and the scale information.

In yet another possible embodiment, the third determining module referred to above includes:

the extraction module is configured to extract target historical access information within a preset time period from the historical access information;

the fifth acquisition module is configured to perform acquisition of the predicted proportion information of the target account group, wherein the predicted proportion information is used for representing and predicting the proportion of a second account in the target account group in the platform account in a preset time period;

and the fourth determination module is configured to determine the state information according to the target historical access information, the prediction proportion information, the target similarity and the proportion information.

In yet another possible embodiment, the fourth determination module referred to above is configured to perform,

Z＝1+1/B1*D*(1-T)*B2

In yet another possible embodiment, the first obtaining module referred to above includes:

the fifth determining module is configured to determine the target account group category to which the target account group belongs according to the state information of the target account group;

and the sixth acquisition module is configured to execute the acquisition of the category of the recommendation information corresponding to the target account group category from the preset association information between the account group category and the category of the recommendation information.

In a further possible embodiment, the fifth determining module mentioned above is configured to perform, in a case that the status information of the target account group includes an average duration of access to the application program by the second account in the target account group, determining that the account group category is the target account group category if the average duration corresponding to the target account group falls within the duration range of the account group category.

According to a third aspect of embodiments of the present disclosure, there is provided a server, which may include:

a processor;

a memory configured to store processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the information recommendation method as shown in any embodiment of the first aspect.

According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, wherein instructions of the computer-readable storage medium, when executed by a processor of a server, cause the server to implement the information recommendation method as shown in any one of the embodiments of the first aspect.

According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product comprising a computer program stored in a readable storage medium, from which at least one processor of an apparatus reads and executes the computer program, so that the apparatus performs the information recommendation method shown in any one of the embodiments of the first aspect.

The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:

the method and the device for recommending the multimedia information to the first account have the advantages that the first account of the login application program is divided into groups, the target account group corresponding to the first account is determined, then the type of the recommendation information corresponding to the state information is obtained according to the state information of the target account group, and the multimedia information corresponding to the type of the recommendation information is recommended to the first account. Therefore, information can be recommended to the accounts in the corresponding account groups according to the state information of the different account groups, so that the different groups correspond to different recommended contents, the method of recommending to the accounts on the application program platform based on the relatively fixed account groups at present is eliminated, targeted recommendation service can be performed to the accounts of various groups, and the accuracy of information recommendation is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.

FIG. 1 is an architecture diagram illustrating one type of information recommendation, according to an exemplary embodiment;

FIG. 2 is a diagram illustrating an application scenario for information recommendation, according to an exemplary embodiment;

FIG. 3 is a flow diagram illustrating a method of information recommendation, according to an example embodiment;

FIG. 4 is a block diagram illustrating the structure of an information recommendation device according to an exemplary embodiment;

FIG. 5 is a block diagram illustrating a computing device, according to an example embodiment.

Detailed Description

In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in other sequences than those illustrated or described herein. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the disclosure, as detailed in the appended claims.

At present, service platforms corresponding to some application programs often face a dilemma that a user group is relatively fixed. For example, the old users in the service platform use the moderate power of the service platform, and the maintenance cost is low. But the large number of old users may skew the display interface morphology of the content and design recommended in the application to the old users. The reason is that to improve or upgrade these recommended content and designs, the user's feedback information needs to be obtained and the recommended content and design evaluated before improvement of the recommended content and design can be achieved. If only the feedback information of the old user is obtained, the improved recommended content and design are also inclined to the old user, so that accurate information recommendation for most or even all users corresponding to the application program cannot be realized.

The following can be illustrated by way of an example: the service platform can select some related specific users, implement the recommendation information A to recommend information to the specific users, then obtain feedback information of the specific users, and evaluate whether the recommendation information meets the requirements of the service platform. However, this method of obtaining the feedback information needs to construct a user list for the domain knowledge of the specific information a, and if the quality of the recommendation information a is evaluated only by the feedback information of the user, it is easy to have subjectivity and cause evaluation errors. In addition, the method is difficult to generalize, different recommendation strategies often need to reselect specific users, and therefore the calculation amount for selecting the specific users is increased, and the application range is small. In addition, the above evaluation mode is only evaluation by itself, and cannot give the optimization direction of the recommendation information, that is, the above mode cannot be organically combined with the upgrade and optimization of the recommendation system. Thus, a method for organically combining the evaluation method and the information recommendation is needed.

In view of this, the embodiment of the present disclosure determines a state for characterizing the application program used by the prediction account in the prediction target account group by using the optimization of the number of the long-term daily active accounts in the service platform as a core traction, so as to implement an evaluation manner combining the proportion information of the target account group in the platform account, the access information of each second account in the target account group to access the application program, and the target similarity between the account and the target account group, obtain the state information of the target account group, and select the recommendation information by combining the state information.

Therefore, the information recommendation method provided in the embodiment of the present disclosure may be applied to the architecture as shown in fig. 1, and is specifically described in detail with reference to fig. 1.

FIG. 1 is an architectural diagram illustrating one type of information recommendation, according to an exemplary embodiment.

As shown in fig. 1, the information recommendation system includes a client 10 and a server 20.

Wherein the client 10 comprises an application installed on the electronic device. The application program may include various types of applications, such as: a (long or short) video playing application, a music playing application, an instant messaging application and other various applications.

The server 20 may include a service platform corresponding to the application program, and the function of implementing the service platform may be implemented by a server or a cloud server. In the embodiments of the present disclosure, a server is taken as an example for description.

Based on this, the description is made in conjunction with the steps in fig. 2, such as step 210 in fig. 2: the server may obtain a plurality of accounts registered on the application program, and group the plurality of accounts according to the similarity of the account information of the accounts to obtain a plurality of account groups. Wherein the group attributes of each of the plurality of account groups are different. The account information includes at least one of the following: account profile information, geographic location, priority information for geographic location.

Therefore, different contents can be conveniently recommended to accounts of different groups, and the recommended information can be conveniently evaluated according to the state information of different account group groups, so that the service platform can better obtain the data of the application program used by different types of users, and different multimedia information can be recommended according to the state information of the different types of users.

In this way, when a recommendation request sent by the first account of the login application is received, the recommendation request is used for requesting to acquire multimedia information. As in step 220 of fig. 2: and responding to the recommendation request, determining a target account group corresponding to the first account, wherein the target account group comprises a second account, the similarity between the first account information of the first account and the second account information of the second account is greater than or equal to a preset similarity, and the first similarity is determined by the social relationship information between the first account and the second account.

Then, as in step 230 in FIG. 2: according to the state information of the target account group, acquiring the category of recommended information corresponding to the state information, wherein the state information is used for representing and predicting the state of the application program used by the second account; multimedia information corresponding to the category of the recommendation information is recommended to the first account. Therefore, information can be recommended to the accounts in the corresponding account groups according to the state information of the different account groups, so that different groups correspond to different recommended contents, the current method of recommending to the accounts on the application program platform based on the relatively fixed account groups is eliminated, the targeted recommendation service can be favorably carried out on the accounts of various groups, and the accuracy of information recommendation is improved.

Based on the above architecture for information recommendation, the state information in the embodiment of the present disclosure may be applied to other scenarios besides acquiring the category of recommendation information corresponding to the state information, and recommending multimedia information to a user based on the category of recommendation information, which is specifically as follows:

as shown in fig. 2, steps 240 and 250 in fig. 2: first, the status information may be used as an evaluation index for evaluating categories of products, such as display interfaces and/or recommendation information, introduced by the service platform.

The state information of each account group of a plurality of account groups under different products can be calculated through A/B experiments and other modes. The higher the index value characterized by the status information, the higher the number of total accounts expected by the application in a preset time period and the higher the activity of the users in the application. In this way, new products such as display interfaces and/or application functions may be generated based on the status information of the account group.

Secondly, as step 260 in fig. 2: and optimizing the current information recommendation model by using the state information of the account group.

Wherein the status information of the account group may be used as a factor in adjusting a loss function in the information recommendation model. For example, in user click-through rate model training for recommended content, the loss function is redefined using the status information for account groups, and the samples are reweighed with the status information for each account group. Thus, the recommendation model may focus more on the accuracy of the prediction of whether a user has high attributes of the state information. Similarly, weighting the state information for an account group may also be applied to a series of models, such as training to generate an antagonistic neural network model, such as a CF-GAN network model, or a relevance ranking model, i.e., an L2R model.

According to the above architecture and application scenario, the following describes in detail the information recommendation method provided by the embodiment of the present disclosure with reference to fig. 3, where the information recommendation method may be executed by a server in the server shown in fig. 1, and the embodiment of the present disclosure does not limit this.

Fig. 3 is a flow chart illustrating an information recommendation method according to an example embodiment.

As shown in fig. 3, the information recommendation method may specifically include the following steps:

first, in step 310, a recommendation request sent by a first account of a login application is received, where the recommendation request is used to request to acquire multimedia information.

Next, in step 320, in response to the recommendation request, a target account group corresponding to the first account is determined, where the target account group includes the second account, a similarity between the first account information of the first account and the second account information of the second account is greater than or equal to a preset similarity, and the first similarity is determined by social relationship information between the first account and the second account.

In step 330, a category of the recommended information corresponding to the status information is obtained according to the status information of the target account group, and the status information is used for representing and predicting the status of the application used by the second account.

Then, step 340 recommends multimedia information corresponding to the category of the recommended information to the first account.

Therefore, the first account of the login application program is divided into groups, the target account group corresponding to the first account is determined, then the category of the recommendation information corresponding to the status information is acquired according to the status information of the target account group, and the multimedia information corresponding to the category of the recommendation information is recommended to the first account. Therefore, information can be recommended to the accounts in the corresponding account groups according to the state information of the different account groups, so that different groups correspond to different recommended contents, the current method of recommending to the accounts on the application program platform based on the relatively fixed account groups is eliminated, the targeted recommendation service can be favorably carried out on the accounts of various groups, and the accuracy of information recommendation is improved.

The above steps are described in detail below, specifically as follows:

first, in a possible embodiment, before step 320, the information recommendation method may further include:

grouping the accounts according to the similarity of the account information to obtain a plurality of account groups; wherein the plurality of account groups includes a target account group.

Here, the account information includes at least one of the following information: account image information, region priority. The account image information includes image information of age, sex, contact information, etc. of the user corresponding to the account.

Based on the above, a plurality of accounts can be grouped according to the similarity of regions and ages, if the regions are all shown in the region A and the ages are not more than 5 years, the accounts can be divided into an account group.

Based on this, the step 320 may specifically include:

matching the first account information with account information of accounts in each account group to obtain the similarity of the first account and each account group; if the similarity between the first account and the account group is greater than the preset similarity, the account group is determined as a target account group.

The embodiment of the present disclosure provides two ways of determining a target account group, which are specifically as follows:

the first method is as follows: and acquiring the similarity between the behavior information of the first account and the behavior information of each account in each account group, and weighting the multiple similarities to obtain the similarity between the first account and each account group. Then, an account group with similarity greater than the preset similarity is selected from the similarities between the first account and each account group, and the account group with similarity greater than the preset similarity is determined as a target account group.

The second method comprises the following steps: and acquiring the similarity between the behavior information of the first account and the behavior information of each account in each account group, and weighting the multiple similarities to obtain the similarity between the first account and each account in each account group. Then, the first N second accounts with the similarity greater than the preset threshold with the first account are selected from the similarities between the first accounts and the accounts in the account groups, and then the account group with the largest number of the N second accounts in the account groups is counted as the target account group.

Here, in the embodiment of the present disclosure, the accounts may be divided into account groups with the finest granularity according to the dimensions of age, gender, region priority level, and the like, which are related in the account information, and the inter-group difference may be maximized according to a decision tree model in the decision tree analysis method. Here, the decision tree analysis is a risk-type decision method that compares different schemes in a decision with a tree in a graph theory using a probability to obtain an optimal scheme. Then, after a grouping suggestion is given through the decision tree, a group which can contain the smallest cadiel product of all the final classifications of the decision tree is adopted to determine a final target account group.

Secondly, in a possible embodiment, the second account includes a fourth account, and the fourth account is a second account in the target account group, in which the frequency of accessing the application program is greater than or equal to the preset frequency. Thus, before step 330, the information recommendation method may further include:

according to the access information of each second account access application program in the target account group, summarizing the historical access information of the target account group;

Here, the reason why the 3 parameters of the history access information, the object similarity, and the scale information are selected in the embodiment of the present disclosure is as follows, calculating proportion information of each account group obtained by grouping according to census data, constructing a quadratic regression model of the historical access information, the target similarity and the proportion information of the account groups by using the historical access information of each account group of the application program, and adding a new account and the number of reflow accounts to control the influence of the application program to acquire the new account (wherein, a reflow user is a user who returns again in the month within a preset time period for more than 7 days, the silence here may refer to downloading the application program again or having no usage record, and similarly, the return may refer to downloading the application program again or having usage record), and predicting the proportion information of each account group in the future, such as half a year later. Based on this quadratic regression model, an estimate of this parameter of the status information of the account group can be obtained. In addition, the predicted effect of the quadratic regression model is evaluated by using a preset test set, and the increment of the calculated proportion information and the proportion information of each account group in the future, such as half a year, is 2.7%. This indicates that the quadratic regression model has a small error and can accurately reflect the change of the scale information, and thus, the embodiment of the present disclosure determines the status information of the account access application in the account group according to the historical access information, the target similarity, and the scale information.

Here, the embodiment of the present invention provides a way to calculate the historical access information, the target similarity, and the scale information, which is specifically shown as follows.

(1) Computing historical access information

The information recommendation method may further include: and obtaining historical access information according to the access frequency of each account in the target account group to access the application program.

For example, short-term sensitivity indicators are used to fit medium and long-term user access information through machine learning and causal analysis methods. Wherein the historical access information includes a retention rate, i.e., a frequency with which the application is currently accessed, and a prediction of future continued access to the application. Although important, user access information (e.g., access after 30 days to the account, i.e., long retention) is difficult to observe in the short term and is highly fluctuating, and thus, medium and long term retention is mapped to short term index retention using methods of machine learning and causal analysis. The aspect can be realized through an A/B index system, and meanwhile, long-term access information in the application program in the future can be fitted through short-term historical access information of the user, and accounts are definedThe comprehensive index of the user group k is sigma_kθ_kf(α_1k，α_2k，...，α_nk) Where ajk is the jth short-term index, Σ, of the account group k_kθ_kf(α_1k，α_2k，...，α_nk) The larger the historical access information, e.g., long-term retention, of the account group, where n and j are both positive integers.

(2) Calculating target similarity

Acquiring first behavior information of a first account access application program and second behavior information of a fourth account access application program;

and obtaining target similarity according to the Euclidean distance between the first behavior information and the second behavior information, wherein the target similarity is used for representing the similarity of the first account and the fourth account for accessing the application program.

The following method is used to specifically describe the method for obtaining the target similarity:

here, the method of calculating the target similarity is to select an account with higher liveness in the target account group as a representative of all accounts in the entire target account group. Second, the similarity of the first account to the target group of accounts is measured using the similarity of behavior between the account with the higher liveness and the first account. For example, the target similarity may be calculated using all new users from pre-installed channels and sampling a portion of the IOS system as the first account, and sampling the IOS system's accounts as the more active accounts in the target account group.

Here, the reason why the pre-installed channel is adopted is that the channels of the part of new users do not have selective deviation like channels of information flow advertisements and the like when selecting to open the application program, but the pre-installed channels are generally android system users, so that the embodiment of the present disclosure supplements a part of users of the random IOS system as samples. In principle the construction of the sample can also be made by more rich methods, such as adding a new first account pulled by other operational activities of some applications.

Then, the target similarity calculation method may include a step of finding, for an active account in the application program involved in one target account group i, an intersection of the active account and all exposed videos of the new first account from the target account group i. This allows the euclidean distance between the video end rate of this active account and the video end rate of the generally new first account on these videos to be found. Finally, a decreasing function with a similarity of l2 distance smoothed to between 0 and 1 may be defined.

In order to ensure the reasonableness of the target similarity pattern determined as described above, verification may be performed as follows. As shown in table 1, the calculation of the target similarity is exemplified.

TABLE 1

Based on table 1, the euclidean distance between the new first account and the target account group k where the active account is located is Dk ═ [ (S1-O1)2+ (S2-O2)2+ (S3-O3)2 ]/the number of video IDs, and the larger the value of Dk, the larger the difference between the new and old users is, the lower the similarity is. Then, in order to reduce the amount of calculation, all 1/Dk are finally normalized to be in the range of 0-1, and the target similarity is obtained.

(3) Determining ratio information

And determining proportion information according to the second account number in the target account group and the total number of the accounts in the application program. Here, the ratio information may include user permeability.

Based on this, the state information of the application program can be determined in the following manner, which is specifically shown as follows:

acquiring the prediction proportion information of the target account group, wherein the prediction proportion information is used for representing and predicting the proportion of a second account in the target account group in the platform account in a preset time period; here, the prediction ratio information may be calculated from the target similarity and the historical access information.

Wherein the state information may be calculated according to the following formula (1):

Z＝1+1/B1*D*(1-T)*B2 (1)

Based on this, the formula is described by the following example, where B1 is 0.55, it means that the second account in the target account group accounts for about half of the platform account in 30 days, that is, 55%; when D is 0.8, it means that the first behavior information of the first account access application is very similar to the second behavior information of the fourth account access application in the target account group, and the similarity is 80%, where the target similarity is higher means that the first account is a more popular account in the target account group as a whole; when T is 0.6, the sum of the access information of the access application programs of the accounts of the target account group reaches 60% in 30 days, namely the access information of the access application programs of the second account in the target account group accounts for more than half each day; at B2 of 0.5, it can be shown that the second account in the target account group accounts for about half of the platform account in 30 days after the next half year, i.e. 50%, meaning that the target account group has a high retention in the platform and is likely to continue using the application for at least 30 days after the half year. Therefore, the data is substituted into the formula (1), that is, Z is 1+1/0.55 0.8 (1-0.6) 0.5, and Z is 12.36 greater than 10, that is, the probability that the second account in the target account group continues to use the application program is high within 30 days after the next half year, and of course, the new product form proposed by the application program is also resource-skewed toward the product direction of the target account group accessing the application program.

Because the target similarity is determined in a new manner and the prediction proportion information is determined by the target similarity in the embodiment of the present disclosure, a reason for calculating the prediction proportion information and a process for verifying the prediction proportion information are also provided in the embodiment of the present disclosure, which are specifically as follows:

first, the marginal effect of historical access information on the future scale information can be explained to a large extent by the target similarity. This gain should come from two aspects. One is that a high degree of target similarity characterizes applications as having a high degree of acceptance. Then, assuming the historical access information optimization value from this first account is high, the accounts in the target account population can be quickly assimilated once exposed to the application. Therefore, at a high degree of similarity of objects, the effect of improving access information is greater. Secondly, if the target similarity is higher, a stronger social attribute is meant. A high target similarity means that the first account is a larger population of accounts under the target account population, and may also indicate that accounts in the target account population should be a larger population under the target account population. Thus, the prediction ratio information can be calculated from the target similarity and the historical access information.

Secondly, in a possible embodiment, the step 330 may specifically include:

And if the average time length corresponding to the target account group belongs to the time length range of the account group category, determining that the account group category is the target account group category.

For example, the duration occupied by the state of each content of the application used by the target account group may be obtained according to the state information of the target account group, and the category of the recommendation information corresponding to the category of the target account group corresponding to the duration occupied may be found. The content includes product information and/or recommendation information corresponding to the product information. If the duration of the application content 1 used by the target account group is 20 minutes, the category of the recommendation information corresponding to the duration of 20 minutes is category a. If the duration of the application content 1 used by the target account group is 10 hours, the category of the recommendation information corresponding to the duration of 10 hours is category B. Similarly, if the duration of the application content 2 used by the target account group is 50 minutes, the category of the recommendation information corresponding to the duration of 50 minutes is category a. If the duration of the application content 2 used by the target account group is 10 hours, the category of the recommendation information corresponding to the duration of 10 hours is category B.

In this way, the state information of the target account group is used as an evaluation index to generate an index system for evaluating products such as a display interface and/or a recommendation strategy released by a service platform, and in addition, the state information of the target account group is input into a multimedia recommendation model of an application program to obtain the type of recommendation information corresponding to the state information; wherein the multimedia recommendation model is determined by behavior tendency information of a plurality of account groups.

After the status information of the target account group is input into the multimedia recommendation model of the application program and the category of the recommendation information corresponding to the status information is obtained, the method further comprises the following steps:

and taking the state information of the target account group as a sample, and training the multimedia recommendation model until preset training conditions are met to obtain the optimized multimedia recommendation model.

Here, the status information of the target account group may be used to optimize the current information recommendation model. Wherein the status information of the target account group may be factored into adjusting the loss function in the information recommendation model. For example, in user click-through rate model training for recommended content, the loss function is redefined using the state information for the target account group, and the samples are reweighed with the state information for the target account group. In this way, the recommendation model may focus more on the accuracy of whether the user clicks or not, which is high in the attribute of the status information of the target account group. Similarly, weighting the state information for the target account group may also be applied to a series of models, such as training to generate an antagonistic neural network model, such as a CF-GAN network model, or a relevance ranking model, i.e., an L2R model.

In summary, the embodiments of the present disclosure determine a target account group corresponding to a first account by grouping the first account logged in to an application, then obtain a category of recommendation information corresponding to the status information according to the status information of the target account group, and recommend multimedia information corresponding to the category of the recommendation information to the first account. Therefore, information can be recommended to the accounts in the corresponding account groups according to the state information of the different account groups, so that different groups correspond to different recommended contents, the current method of recommending to the accounts on the application program platform based on the relatively fixed account groups is eliminated, the targeted recommendation service can be favorably carried out on the accounts of various groups, and the accuracy of information recommendation is improved.

In addition, the embodiment of the disclosure provides a reusable, generalizable and comprehensive account group status information system. For existing schemes that tend to be temporary and non-generalizable, the derivation of the status information of the account group in the embodiments of the present disclosure is from the perspective of long-term retention optimization and from the inherent attributes of the account group, such as historical access information, target similarity, and scale information. Therefore, the state information of the account group can be repeatedly and widely applied to optimization of various products and recommendation systems without manual participation.

It should be noted that the application scenarios described in the embodiment of the present disclosure are for more clearly illustrating the technical solutions of the embodiment of the present disclosure, and do not constitute a limitation on the technical solutions provided in the embodiment of the present disclosure, and as a new application scenario appears, a person skilled in the art may know that the technical solutions provided in the embodiment of the present disclosure are also applicable to similar technical problems.

Based on the same inventive concept, the disclosure also provides an information recommendation device. The details are described with reference to fig. 4.

Fig. 4 is a block diagram illustrating a structure of an information recommendation apparatus according to an exemplary embodiment.

As shown in fig. 4, the information recommendation device 40 may specifically include:

a receiving module 401 configured to execute receiving a recommendation request sent by a first account of a login application, where the recommendation request is used for requesting to acquire multimedia information;

a first determining module 402, configured to perform determining, in response to the recommendation request, a target account group corresponding to the first account, where the target account group includes a second account, a similarity between first account information of the first account and second account information of the second account is greater than or equal to a preset similarity, and the first similarity is determined by social relationship information between the first account and the second account;

a first obtaining module 403, configured to perform obtaining, according to status information of the target account group, a category of recommendation information corresponding to the status information, where the status information is used to characterize and predict a status of the application used by the second account;

a recommending module 404 configured to perform recommending multimedia information corresponding to the category of the recommended information to the first account.

the plurality of account groups includes a target account group.

In another possible embodiment, the first determining module 402 mentioned above includes:

the matching module is configured to perform matching of the first account information and account information of the accounts in each account group to obtain similarity of the first account and each account group;

and the second determining module is configured to determine the account group as the target account group if the similarity between the first account and the account group is greater than the preset similarity.

the fifth acquisition module is configured to execute acquisition of predicted proportion information of the target account group, wherein the predicted proportion information is used for representing and predicting the proportion of a second account in the target account group in the platform account in a preset time period;

In yet another possible embodiment, the fourth determining module referred to above is configured to perform,

Z＝1+1/B1*D*(1-T)*B2

In yet another possible embodiment, the first obtaining module 403 referred to above includes:

Therefore, the first account of the login application program is divided into groups, the target account group corresponding to the first account is determined, then the type of the recommendation information corresponding to the state information is obtained according to the state information of the target account group, and the multimedia information corresponding to the type of the recommendation information is recommended to the first account. Therefore, information can be recommended to the accounts in the corresponding account groups according to the state information of the different account groups, so that different groups correspond to different recommended contents, the current method of recommending to the accounts on the application program platform based on the relatively fixed account groups is eliminated, the targeted recommendation service can be favorably carried out on the accounts of various groups, and the accuracy of information recommendation is improved.

Based on the same inventive concept, the embodiment of the present disclosure further provides a computing device, which is specifically described in detail with reference to fig. 5.

As shown in fig. 5, the computing device 50 is capable of implementing a block diagram of an exemplary hardware architecture of a computing device according to an information recommendation method and an information recommendation apparatus in the embodiments of the present disclosure. The computing device may refer to a server in embodiments of the present disclosure.

The computing device 50 may include a processor 501 and a memory 502 storing computer program instructions.

Specifically, the processor 501 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present disclosure.

Memory 502 may include a mass storage device configured to store information or instructions. By way of example, and not limitation, the memory 1202 may include a Hard Disk Drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Memory 1202 may include removable or non-removable (or fixed) media, where appropriate. Memory 1202 may be internal or external to the integrated gateway device, where appropriate. In a particular embodiment, the memory 1202 is non-volatile solid-state memory. In certain embodiments, memory 1202 includes Read Only Memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

The processor 501 reads and executes the computer program instructions stored in the memory 502 to perform the following steps:

a processor 501 configured to execute receiving a recommendation request sent by a first account of a login application program, wherein the recommendation request is used for requesting to acquire multimedia information; responding to the recommendation request, determining a target account group corresponding to the first account, wherein the target account group comprises a second account, the similarity between the first account information of the first account and the second account information of the second account is greater than or equal to a preset similarity, and the first similarity is determined by the social relationship information between the first account and the second account; acquiring the category of recommended information corresponding to the state information according to the state information of the target account group, wherein the state information is used for representing and predicting the state of the application program used by the second account; and recommending multimedia information corresponding to the category of the recommended information to the first account.

In one possible embodiment, the processor 501 is configured to perform obtaining account information of a plurality of accounts corresponding to the application; grouping the accounts according to the similarity of the account information to obtain a plurality of account groups; wherein the plurality of account groups includes a target account group.

In another possible embodiment, the processor 501 mentioned above is configured to perform matching the first account information with the account information of the accounts in each account group, so as to obtain a similarity between the first account and each account group; and the second determining module is configured to determine the account group as the target account group if the similarity between the first account and the account group is greater than the preset similarity.

In yet another possible embodiment, the processor 501 mentioned above is configured to, in the case that the second account includes a fourth account, and the fourth account is a second account with a frequency of accessing the application program in the target account group being greater than or equal to a preset frequency, summarize historical access information of the target account group according to access information of each second account accessing the application program in the target account group; calculating the similarity of the first behavior information of the first account access application program and the second behavior information of the fourth account access application program to obtain the target similarity of the first account and the target account group; acquiring proportion information of the target account group, wherein the proportion information is used for representing the proportion of a second account in the target account group in the platform account in a preset time period; and determining status information of the account access applications in the target account group based on the historical access information, the target similarity, and the ratio information.

In yet another possible embodiment, the processor 501 mentioned above is configured to perform, in the historical access information, extracting target historical access information within a preset time period; acquiring the prediction proportion information of the target account group, wherein the prediction proportion information is used for representing and predicting the proportion of a second account in the target account group in the platform account in a preset time period; and determining state information according to the target historical access information, the prediction proportion information, the target similarity and the proportion information.

In yet another possible embodiment, the processor 501 referred to above, is configured to execute,

Z＝1+1/B1*D*(1-T)*B2

In yet another possible embodiment, the processor 501 mentioned above is configured to determine a target account group category to which the target account group belongs according to the status information of the target account group; and acquiring the type of the recommendation information corresponding to the target account group type from the preset association information between the account group type and the recommendation information type.

In yet another possible embodiment, the processor 501 mentioned above is configured to determine that the account group category is the target account group category if the average duration corresponding to the target account group falls within the duration range of the account group category if the status information of the target account group includes the average duration of the second account accessing application program in the target account group.

In one example, the computing device 50 may also include a transceiver 503 and a bus 504. As shown in fig. 5, the processor 501, the memory 502 and the transceiver 503 are connected via a bus 504 to complete communication.

Bus 504 includes hardware, software, or both. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hypertransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Control Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 503 may include one or more buses, where appropriate. Although this disclosed embodiment describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

The embodiment of the present disclosure further provides a computer storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are configured to implement the information recommendation method described in the embodiment of the present disclosure.

In some possible embodiments, various aspects of the methods provided by the present disclosure may also be implemented in the form of a program product including program code configured to cause a computer device to perform the steps of the methods according to various exemplary embodiments of the present disclosure described above in this specification when the program product is run on the computer device, for example, the computer device may perform the information recommendation method described in the embodiments of the present disclosure.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but is not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus and computer program products according to the present disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable control display device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable control display device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable control display device to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable control display device to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps configured to implement the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various changes and modifications can be made in the present disclosure without departing from the spirit and scope of the disclosure. Thus, if such modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is intended to include such modifications and variations as well.

Claims

1. An information recommendation method, comprising:

responding to the recommendation request, determining a target account group corresponding to the first account, wherein the target account group comprises a second account, the similarity between first account information of the first account and second account information of the second account is greater than or equal to a preset similarity, and the first similarity is determined by social relationship information of the first account and the second account;

according to the state information of the target account group, acquiring the category of recommended information corresponding to the state information, wherein the state information is used for representing and predicting the state of the second account using the application program;

2. The method of claim 1, wherein prior to the determining the target group of accounts corresponding to the first account, the method further comprises:

the plurality of account groups includes the target account group.

3. The method of claim 2, wherein determining the target group of accounts corresponding to the first account comprises:

and if the similarity between the first account and the account group is greater than the preset similarity, determining the account group as the target account group.

4. The method according to claim 2, wherein the second account comprises a fourth account, and the fourth account is a second account in the target account group, which accesses the application program more than or equal to a preset frequency;

before the obtaining of the category of the recommendation information corresponding to the status information according to the status information of the target account group, the method further includes:

according to the access information of each second account in the target account group for accessing the application program, summarizing the historical access information of the target account group;

calculating the similarity between first behavior information of the first account accessing the application program and second behavior information of the fourth account accessing the application program to obtain the target similarity between the first account and the target account group;

acquiring proportion information of the target account group, wherein the proportion information is used for representing the proportion of a second account in the target account group in a platform account in a preset time period;

and determining the state information of the application program accessed by the accounts in the target account group based on the historical access information, the target similarity and the proportion information.

5. The method according to claim 1, wherein the obtaining a category of recommendation information corresponding to the status information according to the status information of the target account group comprises:

determining a target account group category to which the target account group belongs according to the state information of the target account group;

and acquiring the category of the recommendation information corresponding to the target account group category from the preset association information between the account group category and the recommendation information category.

6. The method of claim 5, wherein the status information for the target account group comprises an average length of time that a second account in the target account group accesses an application; the determining the target account group category to which the target account group belongs according to the status information of the target account group includes:

and if the average time length corresponding to the target account group belongs to the time length range of the account group type, determining the account group type as the target account group type.

7. An information recommendation apparatus, comprising:

the first determination module is configured to execute determining a target account group corresponding to the first account in response to the recommendation request, wherein the target account group comprises a second account, the similarity between first account information of the first account and second account information of the second account is greater than or equal to a preset similarity, and the first similarity is determined by social relationship information between the first account and the second account;

a first obtaining module configured to obtain a category of recommendation information corresponding to status information of the target account group according to the status information, where the status information is used to characterize a status of predicting that the second account uses the application program;

8. A server, comprising:

a processor;

a memory configured to store the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the information recommendation method of any one of claims 1-6.

9. A computer-readable storage medium, wherein instructions in the computer-readable storage medium, when executed by a processor of a server, cause the processor of the server to implement the information recommendation method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program is stored in a readable storage medium, from which at least one processor of a device reads and executes the computer program to cause the device to perform the information recommendation method according to any one of claims 1 to 6.