CN113407835A - User portrait processing method and server applied to big data online service - Google Patents

User portrait processing method and server applied to big data online service Download PDF

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CN113407835A
CN113407835A CN202110682209.0A CN202110682209A CN113407835A CN 113407835 A CN113407835 A CN 113407835A CN 202110682209 A CN202110682209 A CN 202110682209A CN 113407835 A CN113407835 A CN 113407835A
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杨金明
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Abstract

The user portrait processing method and the server applied to the big data online service can determine the target online service platform corresponding to the target division and treatment result content after the target division and treatment result content is obtained through processing, and send the target division and treatment result content to the target online service platform according to the target portrait updating strategy distributed by the target online service platform, so that the automatic updating of the division and treatment result content is realized, the online service platform does not need to frequently inquire and apply for obtaining the division and treatment result content related to the user portrait in the user portrait treatment server, and the updating efficiency and the updating flexibility of the division and treatment result content related to the user portrait are effectively improved. By updating the content of the target division processing result to the target online business service platform, the target online business service platform can be ensured to position user demand information in real time, and the optimization of related business services is realized.

Description

User portrait processing method and server applied to big data online service
Technical Field
The application relates to the technical field of big data and user portrayal, in particular to a user portrayal processing method and a server applied to big data online service.
Background
User portraits (profiles) play an important role in the big data era, and most of accurate marketing, operation analysis and personalized recommendation based on big data scenes are applied to user portraits which are variable sets for describing user data, so that any real user can be accurately described. And constructing the user portrait, namely labeling the user with various dimensions. From the aspect of business value, the labels and the figures are system modules similar to the middle layer, a foundation is laid for data-driven operation, big data can be helped to 'walk out' of a shrivelled data warehouse, and further diversified services such as personalized recommendation and accurate marketing are performed for users.
With the continuous development of online business services, most of the current online business services are provided through different online business service platforms, and in order to ensure that the online business services can be matched with the requirements of users as much as possible, some online business service platforms can update data information related to the portrait of the users. However, the related online business service platform still has some problems in updating the data information related to the user portrait.
Disclosure of Invention
In view of the foregoing, the present application provides the following.
The scheme of one embodiment of the application provides a user portrait processing method applied to a big data online service, which is applied to a user portrait processing server, and the method comprises the following steps:
performing image division and treatment on the acquired user interaction behavior image to obtain target division and treatment result content;
determining a target online business service platform corresponding to the target division processing result content according to the mapping relation between the division processing result content and the online business service platform;
updating the content of the target division and treatment result to the target online business service platform according to a target portrait updating strategy distributed to the content of the target division and treatment result by the target online business service platform; the target portrait updating strategy is used for indicating the content compression state condition according to when the target dividing and processing result content is updated and the content characteristic description condition of the target dividing and processing result content.
Preferably, the method further comprises:
acquiring online business service information of a selected online business service platform, wherein the online business service information comprises: the selected classification label of the online business service platform, the selected classification label of the division and treatment result content corresponding to the selected online business service platform, and the portrait updating strategy of the selected division and treatment result content;
binding the selected classification label of the online business service platform and the selected classification label of the divide-and-conquer processing result content in the mapping relation;
correspondingly, the determining a target online business service platform corresponding to the target division processing result content according to the mapping relationship between the division processing result content and the online business service platform includes:
determining a target classification label corresponding to the classification label of the target division processing result content according to the mapping relation, and determining an online business service platform indicated by the target classification label as a target online business service platform;
correspondingly, the updating the target portrait processing result content to the target online service platform according to the target portrait updating policy distributed by the target online service platform for the target division processing result content includes:
and determining a target portrait updating strategy distributed by the target online business service platform for the target division processing result content according to the online business service information of the target online business service platform, and updating the target division processing result content to the target online business service platform according to the target portrait updating strategy.
Preferably, the acquiring online service information of the selected online service platform includes:
receiving the item parameter information of the processing item for processing the selected divide-and-conquer processing result content sent by the selected online business service platform;
after finishing distributing the processing items according to the item parameter information, updating the visual content of the online business service of the selected divide-and-conquer processing result content to the selected online business service platform;
receiving online business service information sent by the selected online business service platform, wherein the online business service information is generated by the selected online business service platform according to a graphical content change track acquired from visual content of the online business service;
alternatively, the first and second electrodes may be,
the acquiring of the online service information of the selected online service platform includes:
after detecting the online business service application which is activated by the selected online business service platform and aims at the selected divide-and-conquer processing result content, updating the visual content of the online business service of the selected divide-and-conquer processing result content to the selected online business service platform;
and receiving online business service information sent by the selected online business service platform, wherein the online business service information is generated by the selected online business service platform according to a graphical content change track acquired from the visual content of the online business service.
Preferably, the updating the content of the target division and treatment result to the target online business service platform includes:
acquiring a thread resource statistical result of each update execution thread in a non-occupied state in a plurality of update execution threads;
determining a target updating execution thread according to the thread resource statistical result of the plurality of updating execution threads in the non-occupied state;
updating the content of the target dividing and treating result to the target online business service platform through the target updating execution thread;
or, the updating the content of the target division and treatment result to the target online business service platform includes:
and when the summary result of the thread resources occupied by the to-be-updated divide-and-conquer processing result content including the target divide-and-conquer processing result content in the user portrait processing server is greater than the thread resource statistical result of the user portrait processing server in a non-occupied state, sequentially updating each to-be-updated divide-and-conquer processing result content to a corresponding online service platform according to the divide-and-conquer processing completion time period of each to-be-updated divide-and-conquer processing result content.
Preferably, after the target division and treatment result content is updated to the target online business service platform, the method further includes:
sending update description information of the target divide-and-conquer processing result content to the target online service platform, wherein the update description information comprises: at least one of update status information and update progress information, the update status information comprising: a first update state label used for representing updating, a second update state label used for representing successful updating or a third update state label used for representing failed updating;
wherein, when the update description information includes update status information and the update status information is a third update status tag used for representing that the update is failed, the update description information further includes: updating abnormal tracing information and secondary updating indication information, wherein the updating abnormal tracing information is used for indicating an analysis result of updating failure; the method further comprises the following steps: and when a secondary updating application activated by the target online service platform according to the secondary updating indication information is received, updating the target grading processing result content to the target online service platform again.
Preferably, the updating the target image update policy distributed to the target division and treatment result content to the target online service platform according to the target online service platform includes:
compressing the content of the target divide-and-conquer processing result according to the content feature description condition indicated by the target portrait updating strategy;
and updating the compressed content of the target division and treatment result to the target online business service platform based on the content compression state indicated by the target portrait updating strategy.
Preferably, the online service information further includes: the content storage space of the selected divide-and-conquer processing result content;
correspondingly, the updating the content of the target division and treatment result to the target online business service platform includes:
determining a target content storage space of the target division processing result content according to the online business service information of the target online business service platform;
and updating the target division and treatment result content to the target content storage space of the target online business service platform.
Preferably, the online service information further includes: content feature description labels of the selected divide-and-conquer processing result content;
correspondingly, the updating the content of the target division and treatment result to the target online business service platform includes:
determining a target content feature description label of the target division processing result content according to the online business service information of the target online business service platform;
and marking the target division and treatment result content by adopting the target content feature description label, and updating the marked target division and treatment result content to the target online business service platform.
Preferably, the method further comprises:
acquiring a service interaction scene corresponding to key subscription data in each subscription data directory of target service big data according to the service interaction log of the target online service platform; the service interaction scene corresponding to the key subscription data is a service interaction scene of subscription data of set class service equipment in a corresponding subscription data directory;
clustering the service interaction scenes corresponding to the key subscription data in each subscription data directory based on the correlation coefficient of the service interaction scenes corresponding to the key subscription data in each subscription data directory to obtain at least two cluster sets; the cluster set consists of service interaction scenes corresponding to each key subscription data in the uninterrupted subscription data directory;
scene fusion is carried out on the at least two cluster sets, and at least one service interaction scene cluster is obtained;
and in each subscription data directory, identifying the service interaction scene corresponding to each key subscription data in the service interaction scene cluster as the service interaction scene concerned by the same set class service equipment.
The scheme of one embodiment of the application provides a user portrait processing server, which comprises a processing engine, a network module and a memory; the processing engine and the memory communicate through the network module, and the processing engine reads the computer program from the memory and operates to perform the above-described method.
In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.
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The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
FIG. 1 is a flow diagram illustrating an exemplary user representation processing method and/or process for large data online services, according to some embodiments of the present application;
FIG. 2 is a block diagram illustrating an exemplary user representation processing device for use in a big data online service according to some embodiments of the present application;
FIG. 3 is a block diagram of an exemplary user representation processing system for large data online services, shown in accordance with some embodiments of the present application, an
FIG. 4 is a diagram illustrating the hardware and software components of an exemplary user representation processing server according to some embodiments of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
The overall scheme of the user portrait processing method and the server applied to the big data online service can be summarized as follows: performing image division and treatment on the acquired user interaction behavior image to obtain target division and treatment result content; determining a target online business service platform corresponding to the target division processing result content according to the mapping relation between the division processing result content and the online business service platform; and updating the target division and treatment result content to the target online business service platform according to a target portrait updating strategy distributed to the target division and treatment result content by the target online business service platform.
It can be understood that, according to the scheme, after the target division and treatment result content is obtained through treatment, the target online business service platform corresponding to the target division and treatment result content can be determined, and the target division and treatment result content can be sent to the target online business service platform according to the target portrait updating strategy distributed by the target online business service platform, so that automatic updating of the division and treatment result content is achieved, the online business service platform does not need to frequently inquire and apply for obtaining the division and treatment result content related to the user portrait in the user portrait treatment server, and updating efficiency and updating flexibility of the division and treatment result content related to the user portrait are effectively improved.
The method of the embodiment of the application can be applied to block chain payment service, online office service, remote education service, real-time search service and the like.
For further explanation of the overall scheme, first, an exemplary user representation processing method applied to a big data online service is described, please refer to fig. 1, which is a flowchart illustrating an exemplary user representation processing method and/or process applied to a big data online service according to some embodiments of the present application, and the user representation processing method applied to a big data online service may include the technical solutions described in the following steps S10 to S30.
Step S10: and the user portrait processing server performs portrait dividing and processing on the acquired user interaction behavior portrait to obtain target dividing and processing result content.
In the embodiment of the present application, the basic idea of the divide and conquer algorithm is to decompose a problem with size N into M sub-problems with smaller size, which are independent of each other and have the same property as the original problem. The solution of the original problem can be obtained by solving the solution of the subproblem. I.e. a programmed algorithm that is performed on a per-target basis.
Correspondingly, with the continuous expansion of the number and scale of the user interaction behavior images, if the whole user interaction behavior images are directly processed and analyzed, the user image processing server may face a large data processing pressure, and there is a risk of server crash. Therefore, the embodiment of the application can ensure the integrity and the accuracy of the content of the target division processing result and reduce the data processing pressure of the user portrait processing server by performing portrait division processing (which can also be understood as distributed processing) on the user interactive behavior portrait.
Furthermore, the user portrait processing server performs portrait dividing processing on the acquired user interaction behavior portrait, so that the user interaction behavior portrait can be divided into a plurality of sub portraits, and then different sub portraits are processed and analyzed synchronously or asynchronously through a plurality of threads, so that analysis processing results of different sub portraits are summarized to obtain target dividing and processing result content. In some embodiments, the target divide and conquer process result content may be user requirement information corresponding to the user interaction behavior image, including but not limited to real-time requirements and potential requirements. The process of processing and analyzing different sub-images can be referred to the related art, and will not be described herein.
Step S20: and the user portrait processing server determines a target online business service platform corresponding to the target division processing result content according to the mapping relation between the division processing result content and the online business service platform.
In the embodiment of the application, the mapping relationship is used for recording the corresponding relationship between the content of the divide-and-conquer processing result and the online service platform. It can be understood that the user portrait processing server is in communication with the plurality of online business service platforms, so that for the convenience of managing the plurality of online business service platforms, the pairing condition between different division and treatment result contents and different online business service platforms can be determined based on the mapping relation, and therefore portrait updating instructions or other related information and the like can be issued to the related online business service platforms in time.
In some possible embodiments, the method may further include updating and adjusting the mapping relationship before performing step S20. Further, before performing step S20, the method may further include what is encompassed by embodiment a below: acquiring online business service information of a selected online business service platform, wherein the online business service information comprises: the selected classification label of the online business service platform, the selected classification label of the division and treatment result content corresponding to the selected online business service platform, and the portrait updating strategy of the selected division and treatment result content; and binding the selected classification label of the online business service platform and the selected classification label of the divide-and-conquer processing result content in the mapping relation.
For example, the classification tag T1 of the selected online service platform is used to distinguish the selected online service platform, and the classification tag T2 of the selected grading processing result content corresponding to the selected online service platform is used to distinguish the selected grading processing result content corresponding to the selected online service platform. Furthermore, the classification label T1, the classification label T2 and the sketch updating policy of the selected divide-and-conquer processing result content may be in one-to-one correspondence, so that by binding the classification label of the selected online business service platform and the classification label of the selected divide-and-conquer processing result content in the mapping relationship, updating and adjusting of the mapping relationship can be achieved, which facilitates to quickly and accurately determine the target online business service platform corresponding to the target divide-and-conquer processing result content subsequently.
In the above embodiment a, the step of "obtaining the online service information of the selected online service platform" may be implemented by one of the following embodiments a1 and a 2.
Embodiment a1, the obtaining of the online service information of the selected online service platform may include the following: receiving the item parameter information of the processing item for processing the selected divide-and-conquer processing result content sent by the selected online business service platform; after finishing distributing the processing items according to the item parameter information, updating the visual content of the online business service of the selected divide-and-conquer processing result content to the selected online business service platform; and receiving online business service information sent by the selected online business service platform, wherein the online business service information is generated by the selected online business service platform according to a graphical content change track acquired from the visual content of the online business service.
In the embodiment a1, the processing item may carry the instruction information for processing the selected divide-and-conquer processing result content, or may include the item parameter information (item quantization information) of the processing item, so that different processing items can be assigned according to the item parameter information. In addition, the processing item may further include guidance information related to a visualization processing requirement, so that, after the distribution of the processing item is completed according to the item parameter information, the visualization content (such as a picture or a video) of the online business service of the selected content of the divide-and-conquer processing result may be updated to the selected online business service platform, and then the online business service information sent by the selected online business service platform may be received. Furthermore, the online business service information is generated by the selected online business service platform according to a graphical content change track obtained from the visual content of the online business service, and the graphical content change track can be used for recording the change conditions of different graphical contents, so that the integrity of the online business service information can be ensured.
Embodiment a2, the obtaining of the online service information of the selected online service platform may include the following: after detecting the online business service application which is activated by the selected online business service platform and aims at the selected divide-and-conquer processing result content, updating the visual content of the online business service of the selected divide-and-conquer processing result content to the selected online business service platform; and receiving online business service information sent by the selected online business service platform, wherein the online business service information is generated by the selected online business service platform according to a graphical content change track acquired from the visual content of the online business service.
In embodiment a2, the online service application may be an online service request, which is used to request the content of the result of the division and treatment, and further, the online service platform determines the change trajectory of the graphical content based on the visualized content of the result of the division and treatment, and then generates the online service information in response, so as to ensure the integrity of the online service information.
Further, on the basis of the foregoing embodiment a, the step of determining the target online service platform corresponding to the target division processing result content according to the mapping relationship between the division processing result content and the online service platform described in embodiment B may include the following steps: and determining a target classification label corresponding to the classification label of the target division processing result content according to the mapping relation, and determining an online business service platform indicated by the target classification label as a target online business service platform.
It can be understood that, through the embodiment B, the target online service platform can be accurately determined by combining the mapping relationship and the classification label, and the determination efficiency of the target online service platform is improved.
Step S30: and the user portrait processing server updates the target dividing and treating result content to the target online business service platform according to a target portrait updating strategy distributed to the target dividing and treating result content by the target online business service platform.
In other words, updating the target division and treatment result content to the target online business service platform can be understood as updating the division and treatment result content in the target online business service platform, and using the target division and treatment result content to cover the previous division and treatment result content in the target online business service platform.
In this embodiment, the target portrait updating policy is used to indicate a content compression status according to which the target division processing result content is updated, and a content feature description status of the target division processing result content. Therefore, the target division processing result content is updated to the target online business service platform, so that the target online business service platform can be ensured to position the user demand information in real time, and the optimization of related business services is realized. In addition, the updating of the target dividing and treating result content is automatically and intelligently issued to the target online business service platform by the user portrait treating server, so that the online business service platform is not required to frequently inquire and apply for obtaining the dividing and treating result content related to the user portrait in the user portrait treating server, and the updating efficiency and the updating flexibility of the dividing and treating result content related to the user portrait are effectively improved.
It can be understood that, on the basis of the embodiment B, the step S30 described above, which updates the target division processing result content to the target online service platform according to the target portrait updating policy distributed by the target online service platform for the target division processing result content, may be implemented by the following embodiment C: and determining a target portrait updating strategy distributed by the target online business service platform for the target division processing result content according to the online business service information of the target online business service platform, and updating the target division processing result content to the target online business service platform according to the target portrait updating strategy.
In the embodiment of the application, firstly, a target portrait updating strategy distributed by a target online business service platform for the target dividing and treating result content can be determined through online business service information of the target online business service platform, and then the target dividing and treating result content is updated to the target online business service platform through the target portrait updating strategy, so that the target online business service platform can be ensured to be capable of positioning user demand information corresponding to the user interactive behavior portrait in real time.
In other possible embodiments, in order to ensure timely update of the target divide-and-conquer processing result content in the target online business service platform, the occupation condition of the update execution thread needs to be considered. It is understood that, a plurality of update execution threads (module units for executing the divide and conquer processing result update function, etc.) are included in the user representation processing server, in this case, the update execution threads need to be considered, and for this purpose, the updating of the target divide and conquer processing result content to the target online service platform described in the above step S30 may include the following contents: acquiring a thread resource statistical result of each update execution thread in a non-occupied state in a plurality of update execution threads; determining a target updating execution thread according to the thread resource statistical result of the plurality of updating execution threads in the non-occupied state; and updating the content of the target divide-and-conquer processing result to the target online business service platform through the target updating execution thread.
For example, the unoccupied state may be an idle state, and the thread resource statistics may be quantitative statistics of available thread resources, such as a percentage of all thread resources available. Therefore, the target updating execution thread can be determined according to the thread resource statistical result of the plurality of updating execution threads in the non-occupied state, so that the target dividing and treating result content is updated to the target online business service platform through the target updating execution thread, and the target dividing and treating result content is ensured to be updated in the target online business service platform in time.
In other possible embodiments, the updating of the content of the target divide-and-conquer processing result to the target online service platform in step S30 may include the following steps: and when the summary result of the thread resources occupied by the to-be-updated divide-and-conquer processing result content including the target divide-and-conquer processing result content in the user portrait processing server is greater than the thread resource statistical result of the user portrait processing server in a non-occupied state, sequentially updating each to-be-updated divide-and-conquer processing result content to a corresponding online service platform according to the divide-and-conquer processing completion time period of each to-be-updated divide-and-conquer processing result content.
It can be understood that, if, in the user portrait processing server, the summary result (resource amount percentage 1) of the thread resources occupied by the content of the divide and conquer processing result to be updated, including the content of the target divide and conquer processing result, is greater than the statistical result (resource amount percentage 2) of the thread resources of the user portrait processing server in the non-occupied state, it indicates that the thread resources in the non-occupied state are in a deficient state.
In some related embodiments, the updating the target portrait update policy distributed to the target division processing result content according to the target online service platform in step S30 may include the following steps: compressing the content of the target divide-and-conquer processing result according to the content feature description condition indicated by the target portrait updating strategy; and updating the compressed content of the target division and treatment result to the target online business service platform based on the content compression state indicated by the target portrait updating strategy.
For example, the significant content information in the target division processing result content can be determined through the content feature description condition indicated by the target portrait updating strategy, and then the significant content information is retained, and the compression processing is performed on other content information, so that the data volume of the compressed target division processing result content can be minimized as much as possible. Furthermore, by analyzing the content compression state indicated by the target portrait updating strategy, a corresponding updating execution thread can be selected to update the compressed target dividing and treating result content to the target online business service platform, so that the updating efficiency of the target dividing and treating result content in the target online business service platform is improved.
In some possible embodiments, after the content of the target credit treatment result is updated to the target online service platform in the above step S30, the method may further include the content described in the following step S40. Step S40: and sending the update description information of the target division and treatment result content to the target online business service platform.
In step S40, the update description information includes: at least one of update status information and update progress information, the update status information comprising: the update state tag is used for representing a first update state tag in update, a second update state tag in update success or a third update state tag in update failure.
In some possible embodiments, when the update description information includes update status information, and the update status information is a third update status tag for characterizing that the update fails, the update description information further includes: updating abnormal tracing information and secondary updating indication information, wherein the updating abnormal tracing information is used for indicating an analysis result of updating failure. Based on this, the method may further comprise the following: and when a secondary updating application activated by the target online service platform according to the secondary updating indication information is received, updating the target grading processing result content to the target online service platform again. In this way, the target division and treatment result content can be updated to the target online business service platform again according to the third update status label and the second update application, so that the target division and treatment result content is ensured to be successfully updated to the target online business service platform.
In some further embodiments, the online business service information further comprises: and the content storage space of the selected divide-and-conquer processing result content. Based on this, the updating of the content of the target division and treatment result to the target online service platform described in the above steps may include the following: determining a target content storage space of the target division processing result content according to the online business service information of the target online business service platform; and updating the target division and treatment result content to the target content storage space of the target online business service platform.
In some further embodiments, the online business service information further comprises: and the content characteristics of the selected divide-and-conquer processing result content describe labels. Based on this, the updating of the content of the target division and treatment result to the target online service platform described in the above steps may include the following: determining a target content feature description label of the target division processing result content according to the online business service information of the target online business service platform; and marking the target division and treatment result content by adopting the target content feature description label, and updating the marked target division and treatment result content to the target online business service platform. It can be understood that the target division and treatment result content is marked by adopting the target content feature description tag, so that the marked target division and treatment result content can be ensured to be not influenced by network disturbance as much as possible when being updated to the target online business service platform, and the marked target division and treatment result content and the target online business service platform can be ensured to be accurately paired.
In some optional embodiments, after the content of the target divide and conquer processing result is updated to the target online service platform, the method may further include the following: acquiring a service interaction scene corresponding to key subscription data in each subscription data directory of target service big data according to the service interaction log of the target online service platform; the service interaction scene corresponding to the key subscription data is a service interaction scene of subscription data of set class service equipment in a corresponding subscription data directory; clustering the service interaction scenes corresponding to the key subscription data in each subscription data directory based on the correlation coefficient of the service interaction scenes corresponding to the key subscription data in each subscription data directory to obtain at least two cluster sets; the cluster set consists of service interaction scenes corresponding to each key subscription data in the uninterrupted subscription data directory; scene fusion is carried out on the at least two cluster sets, and at least one service interaction scene cluster is obtained; and in each subscription data directory, identifying the service interaction scene corresponding to each key subscription data in the service interaction scene cluster as the service interaction scene concerned by the same set class service equipment.
In other optional embodiments, the content "acquires, according to the service interaction log of the target online service platform, a service interaction scenario corresponding to the key subscription data in each subscription data directory of the target service big data; the service interaction scene corresponding to the key subscription data is a service interaction scene of subscription data of set class service equipment in a corresponding subscription data directory; clustering the service interaction scenes corresponding to the key subscription data in each subscription data directory based on the correlation coefficient of the service interaction scenes corresponding to the key subscription data in each subscription data directory to obtain at least two cluster sets; the cluster set consists of service interaction scenes corresponding to each key subscription data in the uninterrupted subscription data directory; scene fusion is carried out on the at least two cluster sets, and at least one service interaction scene cluster is obtained; in each subscription data directory, identifying the service interaction scene corresponding to each key subscription data in the service interaction scene cluster as the service interaction scene concerned by the same service device of the set category "may be implemented in the following manner.
And step 100, the user portrait processing server acquires a business interaction scene corresponding to key subscription data in each subscription data directory of the target business big data according to the business interaction log of the target online business service platform.
For example, the user representation processing server may be in communication connection with a plurality of service devices, the target service big data may be generated by the service devices and the user representation processing server in a service interaction process, or may be generated by the service devices and the target online service platform in a service interaction process, for example, the target service big data may be extracted through a service interaction log of the target online service platform, so as to obtain a service interaction scene corresponding to the key subscription data in each subscription data directory of the target service big data. Further, the service interaction scenario corresponding to the key subscription data is a service interaction scenario of subscription data of the set class service device in a corresponding subscription data directory.
In addition, the subscription data directory may be understood as a subscription data record or a subscription data list for summarizing different types of subscription data, including but not limited to a service interaction scenario, a service interaction type, a service interaction object, and the like. On this basis, the service interaction scenario may be some possible online service scenarios, such as a subscription service scenario, a payment service scenario, a browsing service scenario, a query service scenario, and the like. Under these scenarios, the user representation processing server may be in interactive communication with the business device.
In some possible examples, the service device of the setting category may be selected according to actual situations, for example, the setting category may be a service device in which the number of objects communicated reaches a set value, for example, the set value is 10, and then the service device d of the setting category may communicate the number of objects to be 12 or 15.
It can be understood that by acquiring the service interaction scene corresponding to the key subscription data, targeted scene analysis and positioning can be performed on the service devices (relatively important service devices) of the set category, so as to completely determine the service portrait analysis basis of the related scene level.
Step 200, the user portrait processing server clusters the service interaction scenes corresponding to the key subscription data in each subscription data directory based on the correlation coefficient of the service interaction scenes corresponding to the key subscription data in each subscription data directory to obtain at least two cluster sets.
In the embodiment of the present application, the cluster set is composed of service interaction scenes corresponding to each key subscription data in an uninterrupted subscription data directory. The uninterrupted subscription data directory may be a chronologically continuous subscription data directory, and the correlation coefficient may be a pearson correlation coefficient, or a cosine similarity, etc.
Based on this, the service interaction scenes corresponding to the key subscription data in each subscription data directory described in step 200 are clustered based on the correlation coefficient of the service interaction scenes corresponding to the key subscription data in each subscription data directory, so as to obtain at least two cluster sets, which may be the contents described in steps 210 and 220 below.
Step 210, determining a second service interaction scene meeting a preset determination condition from the service interaction scenes corresponding to each key subscription data in a second subscription data directory based on an interaction scene correlation coefficient between a first service interaction scene in the first subscription data directory and the service interaction scenes corresponding to each key subscription data in the second subscription data directory.
In this implementation, the first subscription data directory and the second subscription data directory are two consecutive subscription data directories in the respective subscription data directories; the first service interaction scene is a service interaction scene of a target cluster set corresponding to the first subscription data directory. The target cluster set may be a preset reference cluster set.
Further, the interactive scene correlation coefficient may be determined based on scene features, such as based on a cosine distance between scene feature vectors.
In some possible embodiments, the determining step 210 determines, based on an interaction scene correlation coefficient between a first service interaction scene in the first subscription data directory and a service interaction scene corresponding to each key subscription data in the second subscription data directory, a second service interaction scene that meets a preset determination condition from the service interaction scenes corresponding to each key subscription data in the second subscription data directory. This can also be achieved by the following steps 211 and 212.
Step 211, obtaining a first category correlation coefficient between the first service interaction scenario and a service interaction scenario corresponding to each key subscription data in the second subscription data directory.
For example, the first category correlation coefficient is obtained based on a regional correlation coefficient, a heat correlation coefficient and a description feature correlation coefficient between two service interaction scenes.
For example, the regional correlation coefficient, the heat correlation coefficient, and the description feature correlation coefficient between two service interaction scenarios may be c1, c2, and c3, respectively, and then the first category correlation coefficient c0 may be obtained according to the weighting results of c1, c2, and c 3.
Furthermore, the region correlation coefficient can be determined according to longitude and latitude data of different service interaction scenes, the heat correlation coefficient can be determined according to service interaction heat or service interaction frequency, and the description feature correlation coefficient can be determined according to text content or visual content of the service interaction scenes. Generally, the region correlation coefficient, the heat correlation coefficient, and the description feature correlation coefficient may be calculated according to a correlation calculation formula, for example, the region correlation coefficient may be calculated according to a common location area calculation formula, the heat correlation coefficient may be calculated according to a common search index calculation mode, and the description feature correlation coefficient may be calculated based on a common machine learning model, which is not listed here.
Step 212, determining, as the second service interaction scenario, a service interaction scenario in which the corresponding first category correlation coefficient satisfies the preset determination condition, in the service interaction scenarios corresponding to each key subscription data in the second subscription data directory.
In this embodiment, the preset determination condition may be that the first category correlation coefficient reaches a set correlation coefficient threshold, and the correlation coefficient threshold may be cc, and may generally be 0.8, and in some special cases, may be increased or decreased appropriately on the basis of 0.8, and is not limited herein.
In this way, by implementing the step 211 and the step 212, accurate clustering of different service interaction scenes can be realized by performing quantitative analysis on the correlation coefficient, and the reliability of scene clustering is improved.
In some optional embodiments, the method may further comprise: responding to that no service interaction scene with the corresponding first class correlation coefficient meeting the preset judgment condition exists in service interaction scenes corresponding to each key subscription data in the second subscription data directory, and acquiring a second class correlation coefficient between the first service interaction scene and the service interaction scene corresponding to each key subscription data in the second subscription data directory, wherein the second class correlation coefficient is obtained based on a description feature correlation coefficient between the two service interaction scenes; and determining the service interaction scene, of which the corresponding second category correlation coefficient meets the preset judgment condition, in the service interaction scenes corresponding to each key subscription data in the second subscription data directory as the second service interaction scene.
It can be understood that, if there is no service interaction scenario in which the first category correlation coefficient corresponding to each key subscription data in the second subscription data directory satisfies the preset determination condition in the service interaction scenario corresponding to each key subscription data in the second subscription data directory, it is indicated that the first category correlation coefficient determined based on the regional correlation coefficient, the heat correlation coefficient and the descriptive feature correlation coefficient may be more rigorous, thereby causing the relevant service interaction scenarios to be filtered, and to improve this, the characteristic correlation coefficients may be selected as a basis for determining the second category correlation coefficients, therefore, in the service interaction scene corresponding to each key subscription data in the second subscription data directory, and determining the service interaction scene of which the corresponding second category correlation coefficient meets the preset judgment condition as the second service interaction scene.
It can be understood that, since the second-class correlation coefficient does not consider the region correlation coefficient and the heat correlation coefficient, the absolute value of the correlation coefficient is larger than that of the first-class correlation coefficient, and thus, the determination of the service interaction scene in which the second-class correlation coefficient satisfies the preset determination condition can be realized as much as possible based on the description feature level.
In other possible embodiments, the method may further include: responding to that no second service interaction scene meeting the preset judgment condition exists in service interaction scenes corresponding to each key subscription data in the second subscription data directory, and the number of uninterrupted associated service interaction scenes in the target cluster set is lower than the preset number, and detecting the associated service interaction scenes of the target cluster set in the second subscription data directory based on each service interaction scene in the target cluster set; and adjusting the detected associated service interaction scene into a service interaction scene of the target cluster set in the second subscription data directory.
It can be understood that if there is no second service interaction scene that meets the preset determination condition in the service interaction scenes corresponding to each key subscription data in the second subscription data directory, it indicates that the correlation coefficient of the service interaction scenes corresponding to each key subscription data in the second subscription data directory does not meet the requirement, on this basis, if the number of uninterrupted associated service interaction scenes in the target cluster set is lower than the preset number, it indicates that the cluster saturation or the cluster requirement of the target cluster set has not been expected yet, for this reason, the associated service interaction scenes in the second subscription data directory of the target cluster set may be detected based on each service interaction scene in the target cluster set, and may not be detected by the correlation coefficient, for example, the association service interaction scenario obtained through detection may be adjusted to a service interaction scenario of the target cluster set in the second subscription data directory by determining through a direct communication connection relationship or an indirect communication connection relationship.
Step 220, adjusting the second service interaction scenario to a service interaction scenario of the target cluster set in the second subscription data directory.
It can be understood that when the second service interaction scenario satisfies the preset determination condition, the second service interaction scenario may be classified.
Thus, by implementing the step 210 and the step 220, clustering of service interaction scenes can be realized based on two continuous subscription data directories, so that individual service interaction scenes are prevented from being omitted in the clustering process.
And step 300, the user portrait processing server performs scene fusion on the at least two cluster sets to obtain at least one service interaction scene cluster.
In this embodiment, the manner of obtaining the cluster set may be understood as first clustering, and the manner of performing scene fusion on the cluster set to obtain the service interaction scene cluster may be understood as second clustering, so that, through twice clustering processes, the service interaction scene concerned by the same set class service device may be completely and unmistakably identified as much as possible, thereby providing an accurate and reliable analysis basis for service profile analysis of the same set class service device.
It can be understood that since the region correlation coefficient, the heat correlation coefficient and the description feature correlation coefficient exist between different service interaction scenes, the service interaction scene clustering can also be understood as region clustering, heat clustering or description feature clustering. Thus, scene fusion is performed on the at least two cluster sets, and obtaining at least one service interaction scene cluster can be understood as: and performing scene fusion on the at least two cluster sets to obtain at least one regional cluster.
On the basis of the above contents, the step "perform scene fusion on the at least two cluster sets to obtain at least one regional cluster" may be implemented by the following steps 310 and 320.
And step 310, obtaining a correlation coefficient between the at least two cluster sets.
In some possible embodiments, in order to ensure the integrity of the correlation coefficient between the at least two cluster sets, the obtaining of the correlation coefficient between the at least two cluster sets as described in the above step 310 may be implemented by the following steps 311 to 314.
Step 311, obtaining a region correlation coefficient between a first cluster set and a second cluster set of the at least two cluster sets.
It can be understood that, in the first cluster set, the first subscription data directory in which the service interaction scenario corresponding to the last key subscription data is located before the second subscription data directory in which the service interaction scenario corresponding to the first key subscription data is located in the second cluster set in time sequence order.
Further, the region correlation coefficient between the first cluster set and the second cluster set is similar to the above-mentioned manner of calculating the region correlation coefficient between different service interaction scenes, for example, the longitude and latitude data corresponding to different cluster sets may be subjected to mean value calculation, and then calculated by a related calculation formula, which is not described herein again.
In some possible embodiments, the obtaining of the regional correlation coefficient between the first cluster set and the second cluster set of the at least two cluster sets described in step 311 above may be further implemented by the following steps 3111 and 3112.
Step 3111, based on region information of a service interaction scenario corresponding to each key subscription data in the first cluster set, performing cluster detection in a subscription data directory subsequent to the first subscription data directory to obtain a detection service interaction scenario transmitted from the first cluster set to the second subscription data directory.
For example, the region information may be latitude and longitude information or region identification information customized/quantized according to a relevant rule, and by the region information, region-level-based cluster detection may be performed in a subscription data directory subsequent to the first subscription data directory.
In this embodiment, the detection service interaction scenario that the first cluster set is transferred to the second subscription data directory may be understood as a service interaction scenario that is extracted from the first cluster set and migrated to the second subscription data directory, and this step may be obtained based on a cluster detection result. For example, the detection service interaction scenario is determined according to the directory identifier of the service interaction scenario in the cluster detection result, and if the directory identifier of the service interaction scenario is "1-2", it may be determined that the first cluster set transmits the detection service interaction scenario in the second subscription data directory.
Step 3112, obtaining a region correlation coefficient between the detection service interaction scenario transferred from the first cluster set to the second subscription data directory and the service interaction scenario corresponding to the first key subscription data in the second cluster set, as the region correlation coefficient between the first cluster set and the second cluster set.
Furthermore, by determining a region correlation coefficient between a detection service interaction scene transmitted from the first cluster set to the second subscription data directory and a service interaction scene corresponding to the first key subscription data in the second cluster set, the transmission behavior of the service interaction scene between different subscription data directories can be considered, thereby ensuring the accuracy and reliability of the region correlation coefficient between the first cluster set and the second cluster set.
Step 312, obtaining a heat correlation coefficient between the first cluster set and the second cluster set based on the heat of the service interaction scenario corresponding to the last key subscription data in the first cluster set and the heat of the service interaction scenario corresponding to the first key subscription data in the second cluster set.
In the embodiment of the present application, by considering the heat of the service interaction scene corresponding to the last key subscription data in the first cluster set and the heat of the service interaction scene corresponding to the first key subscription data in the second cluster set, since the heat of the service interaction scene corresponding to the last key subscription data in the first cluster set and the heat of the service interaction scene corresponding to the first key subscription data in the second cluster set are closer in time sequence, the timeliness of the heat correlation coefficients of the first cluster set and the second cluster set can be ensured as much as possible, and the reliability of the heat correlation coefficients is improved.
In some other embodiments, the obtaining the heat correlation coefficient of the first cluster set and the second cluster set based on the heat of the service interaction scenario corresponding to the last key subscription data in the first cluster set and the heat of the service interaction scenario corresponding to the first key subscription data in the second cluster set, which is described in step 312, may include the following steps 3121 and 3122.
3121, based on the heat of the service interaction scenario corresponding to the last key subscription data in the first cluster set, and an initial heat correlation coefficient between the heat of the service interaction scenario corresponding to the first key subscription data in the second cluster set.
It can be understood that the initial heat correlation coefficient is a heat correlation coefficient obtained by performing preliminary calculation according to the heat of the service interaction scene corresponding to the last key subscription data in the first cluster set and the heat of the service interaction scene corresponding to the first key subscription data in the second cluster set.
And 3122, optimizing the initial heat correlation coefficient through a target correlation coefficient optimization strategy to obtain the heat correlation coefficients of the first cluster set and the second cluster set.
Further, the target correlation coefficient optimization policy may be a preset coefficient adjustment algorithm, and a mapping relationship exists between the optimization weight of the target correlation coefficient optimization policy and the effective subscription period interval between the first subscription data directory and the second subscription data directory, where the mapping relationship may be that the optimization weight of the target correlation coefficient optimization policy is inversely correlated with the effective subscription period interval between the first subscription data directory and the second subscription data directory. In this way, the initial heat correlation coefficient is optimized through the target correlation coefficient optimization strategy, and the effective subscription period interval between the first subscription data directory and the second subscription data directory can be taken into consideration, so as to combine the influence of the length of the effective subscription period interval on the heat, so as to improve the accuracy of the heat correlation coefficient of the first cluster set and the second cluster set.
Step 313, obtaining a correlation coefficient of the description characteristics of the first cluster set and the second cluster set based on the description characteristics of the service interaction scene corresponding to at least one key subscription data in the first cluster set and the description characteristics of the service interaction scene corresponding to at least one key subscription data in the second cluster set.
It is to be understood that the descriptive feature may be a descriptive feature vector for the content of the service interaction scenario, and the descriptive feature correlation coefficient of the first cluster set and the descriptive feature correlation coefficient of the second cluster set may be obtained by performing cosine distance calculation or euclidean distance calculation on different descriptive feature vectors.
In some other embodiments, the obtaining of the correlation coefficient of the descriptive characteristics of the first cluster set and the second cluster set based on the descriptive characteristics of the service interaction scenario corresponding to the at least one key subscription data in the first cluster set and the descriptive characteristics of the service interaction scenario corresponding to the at least one key subscription data in the second cluster set, which is described in step 313 above, can be implemented through the following steps 3131-3133.
3131, performing feature splicing on the description features of the service interaction scene corresponding to at least two key subscription data that are not continuous in time sequence order in the first cluster set to obtain the description features of the first cluster set.
Step 3132, performing feature splicing on the description features of the service interaction scenes corresponding to at least two key subscription data which are discontinuous in time sequence order in the second aggregation set to obtain the description features of the second aggregation set.
It can be understood that feature concatenation may be performed on the description features based on a time sequence order, for example, by weighting the description feature vector values in the same feature dimension through a set concatenation coefficient, so as to ensure the integrity of the description features of the first cluster set and the description features of the second cluster set.
Step 3133 obtains a correlation coefficient between the descriptive features of the first set of clusters and the descriptive features of the second set of clusters as a descriptive feature correlation coefficient of the first set of clusters and the second set of clusters.
It is understood that through the above steps 3131-3133, before the feature-describing correlation coefficients of the first and second sets of clusters are calculated, the integrity of the feature-describing correlation coefficients of the first and second sets of clusters, and thus the integrity of the feature-describing correlation coefficients of the first and second sets of clusters, can be ensured by feature concatenation.
Step 314, obtaining a correlation coefficient between the first cluster set and the second cluster set based on a region correlation coefficient, a heat correlation coefficient and a description feature correlation coefficient between the first cluster set and the second cluster set.
It is understood that the manner of obtaining the correlation coefficient between the first cluster set and the second cluster set based on the regional correlation coefficient, the heat correlation coefficient and the characteristic correlation coefficient between the first cluster set and the second cluster set is similar to the manner of obtaining the different categories of correlation coefficients based on the regional correlation coefficient, the heat correlation coefficient and the characteristic correlation coefficient of different service interaction scenarios, and will not be further described herein.
And step 320, performing scene fusion on the at least two cluster sets based on the correlation coefficient between the at least two cluster sets to obtain the at least one regional cluster.
In some possible embodiments, the scene fusion of the at least two cluster sets based on the correlation coefficient between the at least two cluster sets described in step 320 above to obtain the at least one regional cluster may include the following steps 321 to 323.
Step 321, taking the at least two cluster sets as a graph unit, and taking a correlation coefficient between the at least two cluster sets as a label of the association path, so as to generate a visual network topology carrying the label.
In this embodiment, a graph unit may be understood as a graph node in graph Data (graphical Data), an association path may be used to connect different graph nodes, a label of the association path may be used to distinguish the association path, and accordingly, a visualized network topology may be a network topology map, where the network topology map includes a plurality of graph nodes (graph units) that are at least partially connected to each other.
And 322, splitting the network topology of the visual network topology with the label to obtain at least one target key network topology.
In the embodiment of the application, the network topology is split on the visual network topology, so that the regional processing of the visual network topology can be realized, and it can be understood that the target key network topology is one of the regions in the visual network topology. Correspondingly, the network topology splitting is performed on the visual network topology with the label to obtain at least one target key network topology, or the network topology splitting is performed on the visual network topology with the label through a set algorithm to obtain the at least one target key network topology. In this embodiment, the setting algorithm may be a graph node centrality priority matching algorithm and/or a multidimensional feature matching algorithm. Further, graph node centrality characterizes the number of paths connected with the graph nodes, and the graph node centrality priority matching algorithm can be understood as analyzing graph nodes with higher graph node centrality preferentially so as to realize network topology splitting. The multidimensional feature matching algorithm can be realized by means of a kmeans feature clustering algorithm, and is not described in detail herein.
Step 323, performing scene fusion on the cluster sets belonging to the same target key network topology to obtain the at least one regional cluster.
It can be understood that after the visual network topology is regionalized, the accuracy of scene fusion can be improved, the processing pressure of the processing data of the user portrait processing server can be reduced, and the scene clustering efficiency is improved.
In other embodiments, the scene fusion may also be performed in the above manner to obtain a heat cluster or a description feature cluster, which is not further described herein.
Step 400, the user portrait processing server identifies the service interaction scene corresponding to each key subscription data in the service interaction scene cluster as the service interaction scene concerned by the same set class service device in each subscription data directory.
In an actual implementation process, after the cluster set is subjected to scene fusion to obtain a service interaction scene cluster, the service interaction scenes in each subscription data directory can be classified according to the service interaction scene cluster, for example, the service interaction scenes corresponding to each key subscription data in the service interaction scene cluster are identified as the service interaction scenes concerned by the same set type service device, that is, the same set type service device is matched with the concerned service interaction scene set, so that the service interaction scene set concerned by the set type service device can be completely determined, and the subsequent service portrait analysis on the set type service device can be realized according to different service interaction scenes concerned by the set type service device.
For example, for the same setting type service device d, it can be determined that the service interaction scenarios concerned by the setting type service device d are scene1, scene2, scene3, scene4, and scene 5. Therefore, when the set type service equipment d is subjected to portrait analysis subsequently, different service portraits of the set type service equipment d can be mined through five service interaction scenes, namely scene1, scene2, scene3, scene4 and scene5, so that the integrity and the accuracy of the mined service portraits are ensured.
In some optional embodiments, after identifying the service interaction scenario corresponding to each key subscription data in the service interaction scenario cluster as a service interaction scenario concerned by the same service device of a set category as described in step 400, the method may further include the following step 500: and according to the service interaction scene concerned by the set type service equipment, performing service portrait mining on the set type service equipment to obtain a service portrait set corresponding to the set type service equipment.
It can be understood that mining analysis of a service interaction layer and a service demand layer can be performed on a service interaction scene concerned by the set type service equipment, so that a service interaction behavior portrait and a service demand portrait of the set type service equipment under each concerned service interaction scene are obtained, then the service interaction behavior portrait and the service demand portrait of the set type service equipment under each concerned service interaction scene are collected, a service image set corresponding to the set type service equipment is obtained, and thus the integrity of the service image set corresponding to the set type service equipment can be ensured.
In some optional embodiments, the performing, in step 500, service image mining on the set type service device according to the service interaction scenario concerned by the set type service device to obtain a service image set corresponding to the set type service device may include the following contents described in steps 510 to 540.
Step 510, acquiring an interactive content set aiming at a target service interactive scene, wherein the interactive content set comprises at least two pieces of interactive content; and obtaining the service association degree between each interactive content in the interactive content set and the target service interactive scene.
For example, the target service interaction scenario may be one of the service interaction scenarios that the setting class service device is interested in. And the service relevance is used for representing the relevance of each interactive content and the target service interactive scene in a service layer.
Step 520, sorting the interactive contents according to the service association degrees corresponding to the interactive contents, and the service behavior events and the service demand events of the interactive contents to obtain corresponding interactive content queues.
For example, the service behavior event is used to represent service interaction behavior data of the interaction content, the service requirement event is used to represent service interaction requirement data of the interaction content, and sorting the interaction contents may be understood as sorting the interaction contents.
In some optional embodiments, sorting the interactive contents according to the service association degrees corresponding to the interactive contents, and the service behavior events and the service demand events of the interactive contents to obtain corresponding interactive content queues specifically includes: dividing each interactive content according to the service association degree corresponding to each interactive content, and the service behavior event and the service demand event of each interactive content to obtain at least two interactive content subsets; and sorting each interactive content subset, and sorting each interactive content in each interactive content subset respectively to obtain the interactive content queue.
In some optional embodiments, dividing each interactive content according to the service association degree corresponding to each interactive content, and the service behavior event and the service demand event of each interactive content to obtain at least two interactive content subsets, which further may include: weighting the service behavior event and the service demand event of each interactive content according to the service association degree corresponding to each interactive content respectively to obtain the global service interactive event of each interactive content; and clustering the interactive contents according to the global service interaction events of the interactive contents to obtain at least two interactive content subsets.
In some optional embodiments, sorting each interactive content subset, and sorting each interactive content in each interactive content subset respectively to obtain the interactive content queue, further may include: sorting each interactive content subset according to the number of the interactive contents contained in each interactive content subset; and for each interactive content subset, respectively executing the following operations: sorting each interactive content in the interactive content subset according to the service behavior event and the service demand event of each interactive content in the interactive content subset and the content matching condition of the interactive content subset; and generating the interactive content queue based on the sorting result among the interactive content sub-sets and the sorting result of each interactive content in each interactive content sub-set.
Step 530, generating a target service representation feature queue aiming at the target service interaction scene based on the interaction content queue, wherein the target service representation feature queue comprises at least two pieces of target service representation feature information.
For example, a plurality of target business representation feature information may be included in the target business representation feature queue.
And 540, determining a service image set corresponding to the service equipment of the set category according to the target service image feature information included in the target service image feature queue.
It can be understood that the service image sets corresponding to the service devices of the set classes can be determined by summarizing, integrating and classifying the target service image feature information included in the target service image feature queue, for example, a service image set may include related image information in a plurality of target service interaction scenes, such as explicit service demand information and implicit service demand information.
In some alternative embodiments, the obtaining the service association degree between each interactive content in the interactive content set and the target service interaction scenario described in step 510 specifically includes: and respectively inputting each interactive content into a trained service portrait feature generation model, and mining the interactive content of each interactive content based on a dynamic mining network of a service layer in the trained service portrait feature generation model to obtain the service association degree corresponding to each interactive content output by the dynamic mining network.
Based on this, the step 520 of sorting the interactive contents according to the service association degrees corresponding to the interactive contents, and the service behavior events and the service demand events of the interactive contents to obtain corresponding interactive content queues specifically includes: and respectively inputting the interactive contents and the service association degrees corresponding to the interactive contents into an interactive content clustering and sorting network in the trained service portrait feature generation model, clustering and sorting the interactive contents based on the interactive content clustering and sorting network to obtain a first fusion description vector of an information layer output by the interactive content clustering and sorting network, and combining the interactive content information in the first fusion description vector to form the interactive content queue.
Further, the generating a target service representation feature queue for the target service interaction scenario based on the interaction content queue described in step 530 specifically includes: inputting the fusion description vector into a service portrait feature network in the trained service portrait feature generation model, and performing self-supervision interactive content mining based on the service portrait feature network to obtain the target service portrait feature queue output by the service portrait feature network; the trained service portrait feature generation model is obtained by training according to a sample training data set, sample training data in the sample training data set comprises sample interactive content with set correlation features, and the correlation features represent whether the sample interactive content is related to a sample service interactive scene or not.
In this way, by implementing the steps 510 to 540, the service interaction behavior portrait and the service demand portrait of the set type service device in each concerned service interaction scene can be obtained, and then the service interaction behavior portrait and the service demand portrait of the set type service device in each concerned service interaction scene are summarized to obtain the service image set corresponding to the set type service device, so that the integrity of the service image set corresponding to the set type service device can be ensured.
In some optional embodiments, the service image set corresponding to the service device of the set category may be used as a raw material for image partition processing, so as to continue to perform the step of image partition processing on the acquired user interaction behavior image, such as performing image partition processing on the acquired service image set.
In summary, by implementing the steps S10-S30, the content of the target division processing result is updated to the target online service platform, so that the target online service platform can be ensured to locate the user requirement information in real time, thereby implementing optimization of related service. In addition, the updating of the target dividing and treating result content is automatically and intelligently issued to the target online business service platform by the user portrait treating server, so that the online business service platform is not required to frequently inquire and apply for obtaining the dividing and treating result content related to the user portrait in the user portrait treating server, and the updating efficiency and the updating flexibility of the dividing and treating result content related to the user portrait are effectively improved.
Next, in view of the above-mentioned user representation processing method applied to the big data online service, an exemplary user representation processing device applied to the big data online service is further provided in the embodiment of the present invention, as shown in fig. 2, the user representation processing device 200 applied to the big data online service may include the following functional modules.
And the image dividing and processing module 210 is configured to perform image dividing and processing on the acquired user interaction behavior image to obtain target dividing and processing result content.
And the content platform pairing module 220 is configured to determine, according to a mapping relationship between the division processing result content and the online service platform, a target online service platform corresponding to the target division processing result content.
A processing result updating module 230, configured to update the content of the target division and treatment result to the target online service platform according to a target portrait updating policy that is distributed by the target online service platform for the content of the target division and treatment result; the target portrait updating strategy is used for indicating the content compression state condition according to when the target dividing and processing result content is updated and the content characteristic description condition of the target dividing and processing result content.
Then, based on the above method embodiment and apparatus embodiment, the embodiment of the present invention further provides a system embodiment, that is, a user representation processing system applied to a big data online service, please refer to fig. 3, where a user representation processing system 30 applied to a big data online service may include a user representation processing server 10 and an online service platform 20. Wherein the user representation processing server 10 communicates with the online service platform 20 to implement the above method, and further, the functionality of the user representation processing system 30 applied to big data online services is described as follows. The user portrait processing server 10 performs portrait dividing and processing on the acquired user interaction behavior portrait to obtain target dividing and processing result content; determining a target online business service platform 20 corresponding to the target division processing result content according to the mapping relation between the division processing result content and the online business service platform 20; updating the content of the target division and treatment result to the target online business service platform 20 according to a target portrait updating strategy distributed to the content of the target division and treatment result by the target online business service platform 20; the target portrait updating strategy is used for indicating the content compression state condition according to when the target dividing and processing result content is updated and the content characteristic description condition of the target dividing and processing result content.
Further, referring to FIG. 4 in conjunction, user representation processing server 10 may include a processing engine 110, a network module 120, and a memory 130, processing engine 110 and memory 130 communicating through network module 120.
Processing engine 110 may process the relevant information and/or data to perform one or more of the functions described herein. For example, in some embodiments, processing engine 110 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, the Processing engine 110 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.
Network module 120 may facilitate the exchange of information and/or data. In some embodiments, the network module 120 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 120 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 120 may include at least one network access point. For example, the network module 120 may include wired or wireless network access points, such as base stations and/or network access points.
The Memory 130 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 130 is used for storing a program, and the processing engine 110 executes the program after receiving the execution instruction.
It will be appreciated that the configuration shown in FIG. 4 is merely illustrative and that user representation processing server 10 may include more or fewer components than shown in FIG. 4, or may have a different configuration than shown in FIG. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof.
It should be understood that, for the above, a person skilled in the art can deduce from the above disclosure to determine the meaning of the related technical term without doubt, for example, for some values, coefficients, weights, indexes, factors, and other terms, a person skilled in the art can deduce and determine from the logical relationship between the above and the following, and the value range of these values can be selected according to the actual situation, for example, 0 to 1, for example, 1 to 10, and for example, 50 to 100, which are not limited herein.
The skilled person can unambiguously determine some preset, reference, predetermined, set and target technical features/terms, such as threshold values, threshold intervals, threshold ranges, etc., from the above disclosure. For some technical characteristic terms which are not explained, the technical solution can be clearly and completely implemented by those skilled in the art by reasonably and unambiguously deriving the technical solution based on the logical relations in the previous and following paragraphs. Prefixes of unexplained technical feature terms, such as "first", "second", "previous", "next", "current", "history", "latest", "best", "target", "specified", and "real-time", etc., can be unambiguously derived and determined from the context. Suffixes of technical feature terms not to be explained, such as "list", "feature", "sequence", "set", "matrix", "unit", "element", "track", and "list", etc., can also be derived and determined unambiguously from the foregoing and the following.
The foregoing disclosure of embodiments of the present invention will be apparent to those skilled in the art. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present application, and thus the above contents are not an inventive judgment of the overall scheme.
It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

Claims (10)

1. A user portrait processing method applied to a big data online service is characterized by being applied to a user portrait processing server, and the method comprises the following steps:
performing image division and treatment on the acquired user interaction behavior image to obtain target division and treatment result content;
determining a target online business service platform corresponding to the target division processing result content according to the mapping relation between the division processing result content and the online business service platform;
updating the content of the target division and treatment result to the target online business service platform according to a target portrait updating strategy distributed to the content of the target division and treatment result by the target online business service platform; the target portrait updating strategy is used for indicating the content compression state condition according to when the target dividing and processing result content is updated and the content characteristic description condition of the target dividing and processing result content.
2. The method of claim 1, further comprising:
acquiring online business service information of a selected online business service platform, wherein the online business service information comprises: the selected classification label of the online business service platform, the selected classification label of the division and treatment result content corresponding to the selected online business service platform, and the portrait updating strategy of the selected division and treatment result content;
binding the selected classification label of the online business service platform and the selected classification label of the divide-and-conquer processing result content in the mapping relation;
correspondingly, the determining a target online business service platform corresponding to the target division processing result content according to the mapping relationship between the division processing result content and the online business service platform includes:
determining a target classification label corresponding to the classification label of the target division processing result content according to the mapping relation, and determining an online business service platform indicated by the target classification label as a target online business service platform;
correspondingly, the updating the target portrait processing result content to the target online service platform according to the target portrait updating policy distributed by the target online service platform for the target division processing result content includes:
and determining a target portrait updating strategy distributed by the target online business service platform for the target division processing result content according to the online business service information of the target online business service platform, and updating the target division processing result content to the target online business service platform according to the target portrait updating strategy.
3. The method of claim 2, wherein the obtaining online service information of the selected online service platform comprises:
receiving the item parameter information of the processing item for processing the selected divide-and-conquer processing result content sent by the selected online business service platform;
after finishing distributing the processing items according to the item parameter information, updating the visual content of the online business service of the selected divide-and-conquer processing result content to the selected online business service platform;
receiving online business service information sent by the selected online business service platform, wherein the online business service information is generated by the selected online business service platform according to a graphical content change track acquired from visual content of the online business service;
alternatively, the first and second electrodes may be,
the acquiring of the online service information of the selected online service platform includes:
after detecting the online business service application which is activated by the selected online business service platform and aims at the selected divide-and-conquer processing result content, updating the visual content of the online business service of the selected divide-and-conquer processing result content to the selected online business service platform;
and receiving online business service information sent by the selected online business service platform, wherein the online business service information is generated by the selected online business service platform according to a graphical content change track acquired from the visual content of the online business service.
4. The method according to any one of claims 1 to 3, wherein the updating of the content of the target divide-and-conquer processing result to the target online business service platform comprises:
acquiring a thread resource statistical result of each update execution thread in a non-occupied state in a plurality of update execution threads;
determining a target updating execution thread according to the thread resource statistical result of the plurality of updating execution threads in the non-occupied state;
updating the content of the target dividing and treating result to the target online business service platform through the target updating execution thread;
or, the updating the content of the target division and treatment result to the target online business service platform includes:
and when the summary result of the thread resources occupied by the to-be-updated divide-and-conquer processing result content including the target divide-and-conquer processing result content in the user portrait processing server is greater than the thread resource statistical result of the user portrait processing server in a non-occupied state, sequentially updating each to-be-updated divide-and-conquer processing result content to a corresponding online service platform according to the divide-and-conquer processing completion time period of each to-be-updated divide-and-conquer processing result content.
5. The method according to any one of claims 1 to 3, wherein after the content of the target divide-and-conquer processing result is updated to the target online business service platform, the method further comprises:
sending update description information of the target divide-and-conquer processing result content to the target online service platform, wherein the update description information comprises: at least one of update status information and update progress information, the update status information comprising: a first update state label used for representing updating, a second update state label used for representing successful updating or a third update state label used for representing failed updating;
wherein, when the update description information includes update status information and the update status information is a third update status tag used for representing that the update is failed, the update description information further includes: updating abnormal tracing information and secondary updating indication information, wherein the updating abnormal tracing information is used for indicating an analysis result of updating failure; the method further comprises the following steps: and when a secondary updating application activated by the target online service platform according to the secondary updating indication information is received, updating the target grading processing result content to the target online service platform again.
6. The method according to any one of claims 1 to 3, wherein the updating the target segmentation processing result content to the target online business service platform according to the target portrait updating policy allocated by the target online business service platform for the target segmentation processing result content comprises:
compressing the content of the target divide-and-conquer processing result according to the content feature description condition indicated by the target portrait updating strategy;
and updating the compressed content of the target division and treatment result to the target online business service platform based on the content compression state indicated by the target portrait updating strategy.
7. The method according to any of claims 2 to 3, wherein the online service information further comprises: the content storage space of the selected divide-and-conquer processing result content;
correspondingly, the updating the content of the target division and treatment result to the target online business service platform includes:
determining a target content storage space of the target division processing result content according to the online business service information of the target online business service platform;
and updating the target division and treatment result content to the target content storage space of the target online business service platform.
8. The method according to any of claims 2 to 3, wherein the online service information further comprises: content feature description labels of the selected divide-and-conquer processing result content;
correspondingly, the updating the content of the target division and treatment result to the target online business service platform includes:
determining a target content feature description label of the target division processing result content according to the online business service information of the target online business service platform;
and marking the target division and treatment result content by adopting the target content feature description label, and updating the marked target division and treatment result content to the target online business service platform.
9. The method of claim 1, further comprising:
acquiring a service interaction scene corresponding to key subscription data in each subscription data directory of target service big data according to the service interaction log of the target online service platform; the service interaction scene corresponding to the key subscription data is a service interaction scene of subscription data of set class service equipment in a corresponding subscription data directory;
clustering the service interaction scenes corresponding to the key subscription data in each subscription data directory based on the correlation coefficient of the service interaction scenes corresponding to the key subscription data in each subscription data directory to obtain at least two cluster sets; the cluster set consists of service interaction scenes corresponding to each key subscription data in the uninterrupted subscription data directory;
scene fusion is carried out on the at least two cluster sets, and at least one service interaction scene cluster is obtained;
and in each subscription data directory, identifying the service interaction scene corresponding to each key subscription data in the service interaction scene cluster as the service interaction scene concerned by the same set class service equipment.
10. A user representation processing server, comprising a processing engine, a network module and a memory; the processing engine and the memory communicate through the network module, the processing engine reading a computer program from the memory and operating to perform the method of any of claims 1-9.
CN202110682209.0A 2021-06-20 2021-06-20 User portrait processing method and server applied to big data online service Withdrawn CN113407835A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114757721A (en) * 2022-05-25 2022-07-15 淄博至诚电子商务有限公司 Service prediction analysis method and AI (Artificial Intelligence) mining system for joint big data mining

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114757721A (en) * 2022-05-25 2022-07-15 淄博至诚电子商务有限公司 Service prediction analysis method and AI (Artificial Intelligence) mining system for joint big data mining

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