CN114186025A - User portrait index heat prediction method, device, equipment and storage medium - Google Patents
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Abstract
The application relates to the technical field of data processing, and provides a user portrait index heat prediction method, a device, equipment and a storage medium, wherein the method responds to the query operation of a user and determines the category of the user to be predicted and the time to be predicted according to the query operation; acquiring page click data of a user category to be predicted; performing data processing on the page click data according to a preset time sequence dimension and a preset category dimension, and establishing a data interface to be counted; acquiring data to be counted through a data interface to be counted according to the time to be predicted; the method comprises the steps of carrying out heat analysis processing on data to be counted to obtain index heat of a user category to be predicted within time to be predicted, reducing influence on operation efficiency of an online database, accelerating data counting speed, dynamically obtaining accurate index heat according to different prediction time ranges, and improving accuracy of the index heat of a user portrait.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a user portrait index heat prediction method, device, equipment and storage medium.
Background
The user portrait is also called a user role and is an effective tool for delineating target users and connecting user appeal and design direction, and the user portrait is widely applied to various fields. Under the background of the big data era, user information is flooded in a network, each concrete information of a user is abstracted into labels, and the labels are utilized to concretize the user image, so that targeted services are provided for the user. In the actual operation process, the most superficial and life-close words are used to link the attributes and behaviors of the user with the expected data conversion. The different index heat degrees of the user portrait can reflect the activity conditions of the user aiming at different services, and the service can be provided for the user in a targeted manner according to the index heat degrees of the user portrait.
In the related art, page click operations are usually stored in an online database, and when a worker counts the heat of a user portrait index, data processing is usually performed directly in the online database, and through the stored page click operations in the online database, the use of the portrait index is counted using complicated logic in combination with experience of the worker in learning historical data and the like.
However, the statistical method in the prior art affects the efficiency of online operation, the statistical efficiency is low, and the staff can only count by using historical data, and the user portrait index heat cannot be accurately predicted.
Disclosure of Invention
The application provides a user portrait index heat prediction method, device, equipment and storage medium, so that the technical problems that the efficiency of online operation is affected by a statistical method in the prior art, the statistical efficiency is low, and workers can only count by means of historical data and cannot accurately predict the user portrait index heat are solved.
In a first aspect, the present application provides a method for predicting user portrait index popularity, including:
responding to the query operation of a user, and determining the category of the user to be predicted and the time to be predicted according to the query operation;
acquiring page click data of the user category to be predicted;
according to a preset time sequence dimension and a preset category dimension, performing data processing on the page click data, and establishing a data interface to be counted;
acquiring data to be counted through the data interface to be counted according to the time to be predicted;
and carrying out heat degree analysis processing on the data to be counted to obtain the index heat degree of the user category to be predicted in the time to be predicted.
The embodiment of the application provides a user portrait index heat prediction method, a user can define a prediction time range by himself, page click data of a user category to be predicted is obtained first for the user category needing to be inquired, data operation and statistical processing are not needed in an online database, the influence on the operation efficiency of the online database is reduced, the data statistical rate is accelerated, after the page click data are obtained, a public statistical method interface on a time sequence and category dimensions can be established, conditions of the page click data, service types, service indexes and the like in preset time can be accurately obtained through the interface, accordingly, accurate index heat can be obtained dynamically according to different prediction time ranges, and the accuracy of the user portrait index heat is improved.
Optionally, the performing heat analysis processing on the data to be counted to obtain an index heat of the user category to be predicted within the time to be predicted includes:
establishing a prediction model according to the data to be counted;
according to the prediction result of the prediction model, obtaining the probability of different user portrait indexes of the user category to be predicted within the time to be predicted;
and sequencing the probabilities of the different user portrait indexes to obtain the index heat of the user category to be predicted.
According to the method and the device, when the index heat of the user portrait is determined, different prediction models can be dynamically built according to different prediction time ranges through a user-defined interface of the prediction time range, so that a user portrait system is more flexible, every possible prediction situation is not required to be stored, the prediction models can adapt to different application scenes, the flexibility of index heat prediction is improved, the system space is saved, and meanwhile, the accuracy of the index prediction of the user portrait is further improved through a method of the prediction models.
Optionally, the obtaining of the page click data of the user category to be predicted includes:
and acquiring page click data of the user category to be predicted in an online database.
In addition, data processing is not needed in the online database, resource occupation of the online database is reduced, and data processing speed is improved.
Optionally, the preset time series dimension includes a current day, a previous week, a previous two weeks, and a current quarter;
the preset category dimensions comprise a primary category dimension, a secondary category dimension and a tertiary category dimension;
the establishing of the data interface to be counted comprises the following steps:
establishing a time dimension + category dimension + image index click frequency interface;
alternatively, the first and second electrodes may be,
establishing a time dimension + image index click frequency interface;
alternatively, the first and second electrodes may be,
and establishing a category dimension + image index click frequency interface.
Here, the embodiment of the application can obtain the click condition of the user portrait index in a future time range through a user-defined time range (for example, one week, one month, and one quarter in the future), enable the user to flexibly master the heat index type through the category dimension, establish different data interfaces to be counted through the two dimensions, quickly, conveniently, and accurately acquire data to be counted through the access interface, and do not need to download a large amount of data for training, thereby further improving the prediction efficiency and the prediction accuracy.
Optionally, after the performing heat analysis processing on the data to be counted to obtain the index heat of the user category to be predicted within the time to be predicted, the method further includes:
and displaying the index heat.
The user portrait index popularity prediction method provided by the embodiment of the application can display the index popularity to the user, realizes the visualization of the index popularity, is convenient for the user to know various service requirement conditions of the target user category, and improves the user experience.
Optionally, after the performing heat analysis processing on the data to be counted to obtain the index heat of the user category to be predicted within the time to be predicted, the method further includes:
and generating service push information for the user category to be predicted according to the index heat.
The service push information can be generated intelligently and automatically according to the index heat of the user portrait, and user experience is further improved.
Optionally, the performing data processing on the page click data according to a preset time sequence dimension and a preset category dimension includes:
and performing data processing on the page click data through an AWK text processing tool according to a preset time sequence dimension and a preset category dimension.
Compared with the method that complex logic is adopted to process a large amount of data in the online database, the method and the device for processing online transaction data have the advantages that the AWK powerful text analysis tool is used, the data processing efficiency can be improved, meanwhile, the resource occupation of the connected database is reduced, the interference on the online transaction processing speed is avoided, and the prediction efficiency of the user portrait index heat is improved.
In a second aspect, an embodiment of the present application provides a user portrait index popularity prediction apparatus, including:
the determining module is used for responding to the query operation of a user and determining the category of the user to be predicted and the time to be predicted according to the query operation;
the first acquisition module is used for acquiring page click data of the user category to be predicted;
the first processing module is used for carrying out data processing on the page click data according to a preset time sequence dimension and a preset category dimension and establishing a data interface to be counted;
the second acquisition module is used for acquiring the data to be counted through the data interface to be counted according to the time to be predicted;
and the second processing module is used for carrying out heat degree analysis processing on the data to be counted to obtain the index heat degree of the user category to be predicted in the time to be predicted.
Optionally, the second processing module is specifically configured to:
establishing a prediction model according to the data to be counted;
according to the prediction result of the prediction model, obtaining the probability of different user portrait indexes of the user category to be predicted within the time to be predicted;
and sequencing the probabilities of the different user portrait indexes to obtain the index heat of the user category to be predicted.
Optionally, the first obtaining module is specifically configured to:
and acquiring page click data of the user category to be predicted in an online database.
Optionally, the preset time series dimension includes a current day, a previous week, a previous two weeks, and a current quarter;
the preset category dimensions comprise a primary category dimension, a secondary category dimension and a tertiary category dimension;
the first processing module is specifically configured to:
establishing a time dimension + category dimension + image index click frequency interface;
alternatively, the first and second electrodes may be,
establishing a time dimension + image index click frequency interface;
alternatively, the first and second electrodes may be,
and establishing a category dimension + image index click frequency interface.
Optionally, after the second processing module performs heat analysis processing on the data to be counted to obtain an index heat of the user category to be predicted within the time to be predicted, the apparatus further includes:
and the display module is used for displaying the index heat.
Optionally, after the second processing module performs heat analysis processing on the data to be counted to obtain an index heat of the user category to be predicted within the time to be predicted, the apparatus further includes:
and the generating module is used for generating service push information for the user category to be predicted according to the index heat degree.
Optionally, the first processing module is specifically configured to:
and performing data processing on the page click data through an AWK text processing tool according to a preset time sequence dimension and a preset category dimension.
In a third aspect, the present application provides a user portrait index popularity prediction device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the user representation index heat prediction method as described above in the first aspect and in various possible designs of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement the user portrait index heat prediction method as described in the first aspect and various possible designs of the first aspect.
In a fifth aspect, the present invention provides a computer program product comprising a computer program which, when executed by a processor, implements a user portrait index heat prediction method as described above in the first aspect and in various possible designs of the first aspect.
According to the user portrait index heat prediction method, device, equipment and storage medium, a user can customize a prediction time range, page click data of a user category to be predicted are obtained firstly according to the user category needing to be inquired, data operation and statistical processing are not needed in an online database, the influence on the operation efficiency of the online database is reduced, the data statistical rate is accelerated, after the page click data are obtained, a public statistical method interface on a time sequence and category dimensionality can be established, the page click data, the service type, the service index and other conditions in preset time can be accurately obtained through the interface, accordingly, the accurate index heat can be obtained dynamically according to different prediction time ranges, and the accuracy of the user portrait index heat is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic diagram of a user portrait index popularity prediction system according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a method for predicting user portrait index popularity according to an embodiment of the present disclosure;
FIG. 3 is a flowchart illustrating another method for predicting user portrait index popularity according to an embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram illustrating an apparatus for predicting user portrait index popularity according to an embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of a user portrait index popularity prediction apparatus according to an embodiment of the present disclosure.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. 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 present disclosure, as detailed in the appended claims.
The terms "first," "second," "third," and "fourth," if any, in the description and claims of this application and the above-described figures 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 application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the technical scheme of the present application, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the financial data or the personal data are all in accordance with the regulations of the relevant laws and regulations, and do not violate the customs of the public order.
First, terms in the embodiments of the present application are explained:
AWK: a language for processing text files is a text analysis tool.
Linear regression: a regression analysis that models the relationship between one or more independent and dependent variables using the least squares function of an equation, which is a linear combination of one or more model parameters that become the coefficients of the regression.
The user portrait system needs to be based on an effective behavior screening method, a user portrait index is ranked, classified and analyzed in a time period close to the click condition through a worker, a user crowd range which is active recently is provided for the worker, and the worker provides service for the user in a targeted mode according to analysis historical data. For example, in a user image system of a bank, there are various image indexes in the user image system of the bank, and the variety is wide and the data amount is large. With the increase of the demand, the use heat of the user portrait indexes is expected to have a comprehensive sequencing result for analyzing the user groups which are active in the near future and making targeted service schemes for the user groups, so that an efficient and portable method is needed to adapt to the development of different service demands. Due to the limitation of frequent business transactions of banks every day, page clicking operation is stored in an online database, and the use condition of directly using complex logic statistical portrait indexes in the online database may influence the efficiency of online operation, and the statistical efficiency is also low. From the aspect of workers, the active trend of a user in the near future is difficult to obtain due to the lack of experience of data analysis; the users of different groups are distinguished only according to the heat degree of the historical portrait, and the future recent portrait index heat degree dynamic cannot be represented, so that the customized marketing strategy may have deviation; moreover, the existing system predicts that the click heat of the user portrait index is relatively fixed in time, and workers cannot flexibly screen out possible active target user groups in a short term, a medium term and a long term in the future according to the prediction result.
In order to solve the above problems, embodiments of the present application provide a method, an apparatus, a device, and a storage medium for predicting user portrait index heat, where the method first obtains page click data of a user category to be predicted, and does not need to perform data operation and statistical processing in an online database, so that the influence on the operation efficiency of the online database can be reduced, and the data statistical rate can be increased; meanwhile, a user-defined interface is adopted, so that marketers can customize the prediction time points and obtain prediction results of different time range requirements.
Optionally, fig. 1 is a schematic diagram of a user portrait index heat prediction system architecture according to an embodiment of the present disclosure. In fig. 1, the above-described architecture includes at least one of a receiving device 101, a processor 102, and a display device 103.
It is understood that the illustrated structure of the embodiment of the present application does not constitute a specific limitation to the architecture of the user portrait index heat prediction system. In other possible embodiments of the present application, the foregoing architecture may include more or less components than those shown in the drawings, or combine some components, or split some components, or arrange different components, which may be determined according to practical application scenarios, and is not limited herein. The components shown in fig. 1 may be implemented in hardware, software, or a combination of software and hardware.
In a specific implementation process, the receiving device 101 may be an input/output interface or a communication interface.
The processor 102 can acquire the query operation of the user through the receiving device 101, acquire the page click data of the category of the user to be predicted, and do not need to perform data operation and statistical processing in the online database, so that the influence on the operation efficiency of the online database can be reduced, and the data statistical rate is increased; meanwhile, a user-defined interface is adopted, so that marketers can customize the prediction time points and obtain prediction results of different time range requirements.
The display device 103 may be used to display the above results, or may be used to interact with the user through the display device.
The display device 103 may also be a touch display screen for receiving user instructions while displaying the above-mentioned content to enable interaction with a user.
In some embodiments, the display device 103 may be a mobile terminal, a tablet computer, a notebook computer, a liquid crystal display, an OLED display, a projection display device, or the like.
Alternatively, the display device 103 may be a mobile terminal of a worker or a computer device of a banking outlet, and the worker may receive the user index heat sent by the processor 102 through the mobile terminal or the computer device, so that the worker provides the directional service to the user according to the index heat.
It should be understood that the processor may be implemented by reading instructions in the memory and executing the instructions, or may be implemented by a chip circuit.
In addition, the network architecture and the service scenario described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not constitute a limitation to the technical solution provided in the embodiment of the present application, and it can be known by a person skilled in the art that along with the evolution of the network architecture and the appearance of a new service scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.
The technical scheme of the application is described in detail by combining specific embodiments as follows:
optionally, fig. 2 is a schematic flowchart of a method for predicting user portrait index heat according to an embodiment of the present disclosure. The execution subject of the embodiment of the present application may be the processor 102 in fig. 1, and the specific execution subject may be determined according to an actual application scenario. As shown in fig. 2, the method comprises the steps of:
s201: and responding to the query operation of the user, and determining the category of the user to be predicted and the time to be predicted according to the query operation.
In one possible implementation, the user may perform the query through voice input, or touch selection or input on the display interface, or through an input/output device such as a keyboard.
The user can input the prediction time of a certain user category needing to be queried in a self-defined mode, and therefore accurate index prediction is conducted according to the prediction time.
S202: and acquiring page click data of the user category to be predicted.
And acquiring page click data of the user category to be predicted in an online database.
In addition, data processing is not needed in the online database, resource occupation of the online database is reduced, and data processing speed is improved.
Optionally, a common method for data processing is defined in the AWK data processing function, and includes a method for maintaining pull-down data, field splitting and failure data from the online database, where the acquisition of page click data can be realized by the pull-down data in the online database through the AWK data processing function.
Optionally, after the original data is acquired, the original data can be cleaned and converted into data which completely meets the requirements, so that the prediction accuracy is improved.
S203: and performing data processing on the page click data according to the preset time sequence dimension and the preset category dimension, and establishing a data interface to be counted.
Optionally, performing data processing on the page click data according to a preset time sequence dimension and a preset category dimension, including: and performing data processing on the page click data through an AWK text processing tool according to the preset time sequence dimension and the preset category dimension.
Compared with the method that complex logic is adopted to process a large amount of data in the online database, the method and the device for processing online transaction data have the advantages that the AWK powerful text analysis tool is used, the data processing efficiency can be improved, meanwhile, the resource occupation of the connected database is reduced, the interference on the online transaction processing speed is avoided, and the prediction efficiency of the user portrait index heat is improved.
Optionally, the preset time series dimension includes the current day, the last week, the last two weeks, and the current quarter.
The preset category dimensions include a primary category dimension, a secondary category dimension, and a tertiary category dimension.
Optionally, the primary category dimension includes product holding, transaction behavior, and credit and risk indicators. The user can customize the category dimension to determine the service preference classification condition needing prediction.
Establishing a data interface to be counted, comprising: establishing a time dimension + category dimension + image index click frequency interface; or, establishing a time dimension + portrait index click frequency interface; or, establishing a category dimension + image index click frequency interface.
Here, the embodiment of the application can obtain the click condition of the user portrait index in a future time range through a user-defined time range (for example, one week, one month, and one quarter in the future), enable the user to flexibly master the heat index type through the category dimension, establish different data interfaces to be counted through the two dimensions, quickly, conveniently, and accurately acquire data to be counted through the access interface, and do not need to download a large amount of data for training, thereby further improving the prediction efficiency and the prediction accuracy.
S204: and acquiring the data to be counted through a data interface to be counted according to the time to be predicted.
S205: and carrying out heat degree analysis processing on the data to be counted to obtain the index heat degree of the user category to be predicted in the time to be predicted.
The embodiment of the application provides a user portrait index heat prediction method, a user can customize a prediction time range, page click data of a user category to be predicted is obtained first for the user category needing to be inquired, data operation and statistical processing are not needed in an online database, the influence on the operation efficiency of the online database is reduced, the data statistical rate is accelerated, a public statistical method interface on a time sequence and category dimension can be established after the page click data are obtained, conditions such as page click data, service types and service indexes in preset time can be accurately obtained through the interface, accordingly, the accurate index heat can be obtained dynamically according to different prediction time ranges, and the accuracy of the user portrait index heat is improved.
Optionally, in the embodiment of the present application, different prediction models may be dynamically built according to different prediction time ranges, so as to implement accurate prediction of a user portrait index, and accordingly, fig. 3 is a schematic flow diagram of another user portrait index prediction method provided in the embodiment of the present application, as shown in fig. 3, the method includes:
s301: and responding to the query operation of the user, and determining the category of the user to be predicted and the time to be predicted according to the query operation.
S302: and acquiring page click data of the user category to be predicted.
S303: and performing data processing on the page click data according to the preset time sequence dimension and the preset category dimension, and establishing a data interface to be counted.
S304: and acquiring the data to be counted through a data interface to be counted according to the time to be predicted.
The implementation of steps S301 to S304 is similar to that of steps S201 to S204, and is not described herein again.
S305: and establishing a prediction model according to the data to be counted.
Optionally, a prediction model is built using linear regression.
Among them, linear regression is a lightweight prediction model, and especially for a large amount of data, the main advantage is high efficiency. The linear regression prediction model establishing process comprises 4 steps of feature extraction, model construction, model training and prediction.
S306: and obtaining the probability of different user portrait indexes of the user category to be predicted within the time to be predicted according to the prediction result of the prediction model.
S307: and sequencing the probabilities of different user portrait indexes to obtain the index heat of the user category to be predicted.
According to the method and the device, when the index heat of the user portrait is determined, different prediction models can be dynamically built according to different prediction time ranges through a user-defined interface of the prediction time range, so that a user portrait system is more flexible, every possible prediction situation is not required to be stored, the prediction models can adapt to different application scenes, the flexibility of index heat prediction is improved, the system space is saved, and meanwhile, the accuracy of the index prediction of the user portrait is further improved through a method of the prediction models.
Optionally, after performing heat analysis processing on the data to be counted to obtain an index heat of the user category to be predicted within the time to be predicted, the method further includes:
and displaying the index heat.
Here, the index heat may be displayed by the display device 103.
Alternatively, in the embodiment of the present application, in response to a query operation of a user (here, the user may be a worker), after obtaining a query result each time, the index heat may be displayed on a display device such as a mobile terminal of the worker.
Optionally, a preset updating time period may be set, and the index heat is displayed to the staff regularly according to the preset updating time period. It is understood that the preset updating time period may be determined according to actual situations, and the embodiment of the present application is not particularly limited thereto.
The user portrait index popularity prediction method provided by the embodiment of the application can display the index popularity to the user, realizes the visualization of the index popularity, is convenient for the user to know various service requirement conditions of the target user category, and improves the user experience.
Optionally, after performing heat analysis processing on the data to be counted to obtain an index heat of the user category to be predicted within the time to be predicted, the method further includes:
and generating service push information for the user category to be predicted according to the index heat.
Optionally, the service push information may be pushed to the display interface of the user terminal at regular time.
The service push information can be generated intelligently and automatically according to the index heat of the user portrait, and user experience is further improved.
Fig. 4 is a schematic structural diagram of a user portrait index popularity prediction apparatus according to an embodiment of the present disclosure, and as shown in fig. 4, the apparatus according to the embodiment of the present disclosure includes: a determination module 401, a first acquisition module 402, a first processing module 403, a second acquisition module 404, and a second processing module 405. The user image index heat prediction device may be the processor itself, or a chip or an integrated circuit that realizes the functions of the processor. Here, the division of the determining module 401, the first obtaining module 402, the first processing module 403, the second obtaining module 404, and the second processing module 405 is only a division of one logic function, and both may be integrated or independent physically.
The device comprises a determining module, a judging module and a predicting module, wherein the determining module is used for responding to the query operation of a user and determining the category and the time of the user to be predicted according to the query operation;
the first acquisition module is used for acquiring page click data of a user category to be predicted;
the first processing module is used for carrying out data processing on the page click data according to a preset time sequence dimension and a preset category dimension and establishing a data interface to be counted;
the second acquisition module is used for acquiring the data to be counted through the data interface to be counted according to the time to be predicted;
and the second processing module is used for carrying out heat degree analysis processing on the data to be counted to obtain the index heat degree of the user category to be predicted in the time to be predicted.
Optionally, the second processing module is specifically configured to:
establishing a prediction model according to data to be counted;
according to the prediction result of the prediction model, obtaining the probability of different user portrait indexes of the user category to be predicted within the time to be predicted;
and sequencing the probabilities of different user portrait indexes to obtain the index heat of the user category to be predicted.
Optionally, the first obtaining module is specifically configured to:
and acquiring page click data of the user category to be predicted in an online database.
Optionally, the preset time series dimension includes the current day, the last week, the last two weeks, and the current quarter;
the preset category dimensions comprise a primary category dimension, a secondary category dimension and a tertiary category dimension;
the first processing module is specifically configured to:
establishing a time dimension + category dimension + image index click frequency interface;
alternatively, the first and second electrodes may be,
establishing a time dimension + image index click frequency interface;
alternatively, the first and second electrodes may be,
and establishing a category dimension + image index click frequency interface.
Optionally, after the second processing module performs heat analysis processing on the data to be counted to obtain the index heat of the user category to be predicted within the time to be predicted, the apparatus further includes:
and the display module is used for displaying the index heat.
Optionally, after the second processing module performs heat analysis processing on the data to be counted to obtain the index heat of the user category to be predicted within the time to be predicted, the apparatus further includes:
and the generating module is used for generating service push information for the user category to be predicted according to the index heat degree.
Optionally, the first processing module is specifically configured to:
and performing data processing on the page click data through an AWK text processing tool according to the preset time sequence dimension and the preset category dimension.
As shown in fig. 5, the user portrait index popularity prediction apparatus includes: a processor 501 and a memory 502, the various components being interconnected using different buses, and may be mounted on a common motherboard or in other manners as desired. Processor 501 may process instructions executed within a user representation index heat prediction apparatus, including instructions for graphical information stored in or on a memory for display on an external input/output device (such as a display device coupled to an interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. In fig. 5, one processor 501 is taken as an example.
The memory 502 may be used as a non-transitory computer readable storage medium to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method of the user representation index heat prediction apparatus in the embodiments of the present application (for example, as shown in fig. 4, the determining module 401, the first obtaining module 402, the first processing module 403, the second obtaining module 404, and the second processing module 405). The processor 501 executes various functional applications and user portrayal index heat prediction methods, i.e., methods that implement the user portrayal index heat prediction apparatus of the above-described method embodiments, by executing non-transitory software programs, instructions, and modules stored in the memory 502.
The user portrait indicator heat prediction apparatus may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function control of a user-portrait indicator heat prediction apparatus, such as a touch screen, a keypad, a mouse, or a plurality of mouse buttons, a trackball, a joystick, or other input devices. The output device 504 may be an output device such as a display device of a user-portrait-index heat prediction device. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
The user portrait index heat prediction device of the embodiment of the application can be used for executing the technical scheme of the method embodiments of the application, and the implementation principle and the technical effect are similar, and are not repeated here.
An embodiment of the present application further provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the method for predicting user portrait index heat is implemented by any one of the foregoing methods.
An embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program is configured to implement any one of the above methods for predicting user portrait index heat.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (11)
1. A method for predicting user portrait index heat, comprising:
responding to the query operation of a user, and determining the category of the user to be predicted and the time to be predicted according to the query operation;
acquiring page click data of the user category to be predicted;
according to a preset time sequence dimension and a preset category dimension, performing data processing on the page click data, and establishing a data interface to be counted;
acquiring data to be counted through the data interface to be counted according to the time to be predicted;
and carrying out heat degree analysis processing on the data to be counted to obtain the index heat degree of the user category to be predicted in the time to be predicted.
2. The method according to claim 1, wherein the performing heat analysis processing on the data to be counted to obtain an index heat of the user category to be predicted in the time to be predicted comprises:
establishing a prediction model according to the data to be counted;
according to the prediction result of the prediction model, obtaining the probability of different user portrait indexes of the user category to be predicted within the time to be predicted;
and sequencing the probabilities of the different user portrait indexes to obtain the index heat of the user category to be predicted.
3. The method according to claim 1, wherein the obtaining of the page click data of the user category to be predicted comprises:
and acquiring page click data of the user category to be predicted in an online database.
4. The method of claim 1, wherein the preset time series dimensions include a current day, a previous week, a previous two weeks, and a current quarter;
the preset category dimensions comprise a primary category dimension, a secondary category dimension and a tertiary category dimension;
the establishing of the data interface to be counted comprises the following steps:
establishing a time dimension + category dimension + image index click frequency interface;
alternatively, the first and second electrodes may be,
establishing a time dimension + image index click frequency interface;
alternatively, the first and second electrodes may be,
and establishing a category dimension + image index click frequency interface.
5. The method according to any one of claims 1 to 4, wherein after the performing the heat degree analysis processing on the data to be counted to obtain the index heat degree of the user category to be predicted in the time to be predicted, the method further comprises:
and displaying the index heat.
6. The method according to any one of claims 1 to 4, wherein after the performing the heat degree analysis processing on the data to be counted to obtain the index heat degree of the user category to be predicted in the time to be predicted, the method further comprises:
and generating service push information for the user category to be predicted according to the index heat.
7. The method according to any one of claims 1 to 4, wherein the performing data processing on the page click data according to a preset time series dimension and a preset category dimension comprises:
and performing data processing on the page click data through an AWK text processing tool according to a preset time sequence dimension and a preset category dimension.
8. A user portrait index popularity prediction apparatus, comprising:
the determining module is used for responding to the query operation of a user and determining the category of the user to be predicted and the time to be predicted according to the query operation;
the first acquisition module is used for acquiring page click data of the user category to be predicted;
the first processing module is used for carrying out data processing on the page click data according to a preset time sequence dimension and a preset category dimension and establishing a data interface to be counted;
the second acquisition module is used for acquiring the data to be counted through the data interface to be counted according to the time to be predicted;
and the second processing module is used for carrying out heat degree analysis processing on the data to be counted to obtain the index heat degree of the user category to be predicted in the time to be predicted.
9. A user portrait indicator heat prediction apparatus, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a user representation index heat prediction method as claimed in any one of claims 1 to 7.
10. A computer readable storage medium having computer executable instructions stored thereon for implementing a user representation index heat prediction method as claimed in any one of claims 1 to 7 when executed by a processor.
11. A computer program product comprising a computer program, characterized in that the computer program realizes the method of any of claims 1 to 7 when executed by a processor.
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