Intelligent user behavior analysis platform based on big data and AI

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent user behavior analysis platform based on big data and AI, and the platform comprises a data collection module which collects webpage end data and mobile end data; the data preprocessing module is used for preprocessing the webpage end data and the mobile end data to obtain a user behavior data set; the behavior association analysis module is used for calculating an article category support degree and a target article association confidence degree according to the user behavior data set, and judging a user closeness degree; the user behavior prediction module is used for calculating and judging an article purchase tendency of the target platform; and the copywriting pushing and selecting module is used for calculating an adaptive form of the copywriting of the high-quality target article according to the article purchasing tendency of the target platform, and pushing the adaptive form as the copywriting selected by the user. By means of big data processing and artificial intelligence, data are comprehensively collected and processed, user behaviors are accurately analyzed, copywriting is recommended in a personalized mode, and accurate mastering and efficient meeting of user requirements are achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an intelligent user behavior analysis platform based on big data and AI Background Art

[0002] Big data advertising recommendation technology collects and analyzes massive amounts of user behavior data to mine user preferences and needs. It uses advanced algorithm models to achieve personalized advertising push, improve advertising conversion rates and user satisfaction, and is a key technology in the field of modern digital marketing.

[0003] For example, a Chinese patent with publication number CN118505311A discloses an advertising information recommendation system based on big data, including: a management control module, an analysis and optimization module and a transmission feedback module, characterized in that: the management control module is connected to the analysis and optimization module through a network, the analysis and optimization module is connected to the transmission feedback module through a network, the management control module is used to manage and control mailbox advertising recommendations, the analysis and optimization module is used to analyze and optimize advertising recommendations, and the transmission feedback module is used to perform transmission feedback control of data information. The management control module includes a graph construction module, a node management module and a relationship mining module, the graph construction module is electrically connected to the node management module, the graph construction module is connected to the relationship mining module through a network, and the graph construction module is used to construct and process the knowledge graph of mailbox user information; it can be seen that the advertising information recommendation system based on big data has the problem of insufficient personalization of user behavior analysis and advertising copy recommendation schemes during actual use. Summary of the invention

[0004] To this end, the present invention provides an intelligent user behavior analysis platform based on big data and AI to overcome the problem of insufficient personalization of user behavior analysis and copy recommendation solutions in the prior art.

[0005] To achieve the above objectives, the present invention provides an intelligent user behavior analysis platform based on big data and AI.

[0006] Data collection module, used to collect web page data and mobile terminal data;

[0007] A data preprocessing module, for cleaning the web page data and the mobile terminal data to obtain valid web page data and valid mobile terminal data, for integrating the valid web page data and the valid mobile terminal data to obtain data to be converted, and for converting the data to be converted into a user behavior data set;

[0008] A behavior association analysis module, used to judge the target item category according to the user behavior data set, and also used to calculate the target item association confidence according to the target item category and the target platform item set, and judge the user closeness according to the target item association confidence;

[0009] A user behavior prediction module is used to judge the purchase tendency of the target platform items according to the association confidence of the target items;

[0010] The copy push selection module is used to judge the high-quality target item copy according to the purchase tendency of the target platform items, and is also used to judge the adaptation form of the high-quality target item copy according to the high-quality target item copy form confidence and the high-quality target item copy form support, and push the high-quality target item copy and the adaptation form as user selected copy, and is also used to compare the number of mobile terminal behaviors with the number of web terminal behaviors, and adjust the adaptation form determination process according to the comparison result.

[0011] Furthermore, the data preprocessing module includes a data cleaning unit, which performs noise removal on the web data and the mobile data, and performs missing value processing on the web data and the mobile data after the noise removal. After completing the missing value processing, the data in the web data and the mobile data that does not conform to the business logic is filtered to obtain valid web data and valid mobile data.

[0012] Furthermore, the data preprocessing module also includes a data integration unit, which performs data conflict elimination processing on the data in the valid web data and the valid mobile data, eliminates the conflicting data in the valid web data and the valid mobile data, and performs multi-source data fusion processing on the valid web data and the valid mobile data after the data conflict elimination processing to obtain the data to be converted.

[0013] Furthermore, the data preprocessing module also includes a data conversion unit, which discretizes various types of data in the data to be converted and standardizes the discretized data to be converted to obtain a user behavior data set, wherein the user behavior data set includes basic user behaviors.

[0014] Furthermore, the behavior association analysis module calculates the item category support Support{X} according to the item category behavior item set X and the total item behavior item set N in the user behavior data set, and sets The item support Support{X} is compared with the preset minimum item support Support{X}0, and the target item category is determined according to the comparison result, where:

[0015] When Support{X}≥Support{X}0, the behavior association analysis module determines that the item category behavior item set X is a frequent item set, and uses the item category corresponding to the item category behavior item set X as the target item category;

[0016] When Support{X}<Support{X}0, the behavior association analysis module determines that the item category behavior item set X is a non-frequent item set;

[0017] The count{X} refers to the number of times the item category behavior item set X appears in the user behavior data set, and the preset minimum item support Support{X}0 refers to the preset support value for determining whether the item category behavior item set X is a frequent item set, and is set to 0.3<Support{X}0<1.

[0018] Furthermore, the behavior association analysis module obtains the item category behavior item set of the target item category, takes it as the target item category behavior item set Xa, and calculates the target item association confidence according to the support Support(Xa∪Y) of the union of the target item category behavior item set Xa and the target platform item set Y set up Support(Xa∪Y) is the support of the target item category behavior item set Xa and the target platform item set Y. The behavior association analysis module converts the target item association confidence Minimum confidence level of the preset association rule A comparison is performed, and the user closeness is judged based on the comparison results, where:

[0019] when When , the behavior association analysis module determines that the user closeness is high;

[0020] when When , the behavior association analysis module determines that the user closeness is low;

[0021] The preset association rule minimum confidence Refers to the preset value of the association rule for judging the closeness of users.

[0022] Further, the user behavior prediction module is based on the target item association confidence And the target platform item support Support (Y) calculates the target item promotion degree, set And judge the purchase tendency of the target platform items based on the calculation results, where:

[0023] when When the user behavior prediction module determines that the target platform item has a high purchase tendency, a high-frequency copy recommendation is made for the target platform item;

[0024] when When the user behavior prediction module determines that the purchase tendency of the target platform item is average, a random copy recommendation is made for the target platform item;

[0025] when When the user behavior prediction module determines that the purchase tendency of the target platform item is low, no copy recommendation of the target platform item is performed.

[0026] Furthermore, the copy push selection module selects the target platform item based on the support of the copy A with high purchase tendency (Support(A)), the support of the union of copy A and target user B (Support(A∪B)), and the user copy confidence. Calculate the target item copy acceptance ,set up And judge the high-quality target item copy based on the calculation results, among which:

[0027] when When the target copy is received, the copy push selection module determines that the target copy has a high acceptance rate, and the copy A is a high-quality target item copy, and increases the frequency of pushing the copy A to the user B;

[0028] when When the copy push selection module determines that the target copy has low acceptance, the copy A does not belong to the high-quality target item copy, and the copy A is not pushed to the target user B.

[0029] Furthermore, the copy push selection module selects the copy of the high-quality target item based on its confidence level. And the support degree of the high-quality target item copywriting form Support (D) is used to calculate the improvement degree of the high-quality target item copywriting form set up , and judge the adaptation form of the high-quality target item copy based on the calculation results, where:

[0030] when When the copy push selection module determines that the adaptation form of the high-quality target item copy is appropriate, and pushes it;

[0031] when When the copy push selection module determines that the adaptation form of the high-quality target item copy is inappropriate, the push is cancelled.

[0032] Furthermore, when the adaptation form is the copy length, the copy push selection module compares the number of mobile terminal behaviors P with the number of web page behaviors Q, and adjusts the adaptation form determination process according to the comparison result, wherein:

[0033] When P<Q, no adjustment is made to the adaptation form determination process;

[0034] When P≥Q, adjust the adaptation form determination process and set the adjustment coefficient V=0.6+e -(P-Q)-1 , e is the base of the natural logarithm, and the degree of improvement of the copywriting form of high-quality target items is based on the adjustment coefficient V Adjustments are made to the high-quality target item copywriting.

[0035] The lifting degree is W, set

[0036] Compared with the prior art, the beneficial effect of the present invention lies in that the platform comprehensively collects data from the web and mobile terminals through the data collection module, ensuring the breadth and diversity of the data, and providing a solid foundation for subsequent data analysis and user behavior prediction. The platform data preprocessing module cleans and integrates the collected data, effectively removes noise and redundant information, and improves the accuracy and availability of the data. At the same time, the processed data is converted into a user behavior data set, providing direct and effective data support for subsequent behavior association analysis. Through the analysis of the user behavior data set by the behavior association analysis module, the support of the item category is calculated, and the target item category is accurately judged. Furthermore, the association confidence is calculated according to the target item category and the target platform item set, and the closeness between the user and the item is effectively evaluated. In addition, the calculation order of the associated confidence is adjusted according to the length of the target item category, which optimizes the analysis process and improves the analysis efficiency. The user behavior prediction module calculates the target item promotion degree based on the results of the behavior association analysis and accurately predicts the user's item purchase tendency on the target platform. This step provides a strong basis for the subsequent copy push and helps to achieve precision marketing. The copy push selection module calculates the target item copy acceptance according to the user's purchase tendency and accurately judges the high-quality target item copy. At the same time, the promotion degree is calculated based on the copy form confidence and support, the copy adaptation form is optimized, and the high-quality target item copy and adaptation form are pushed as user selected copy, creating personalized copy design for customers, improving the pertinence and attractiveness of the copy, and helping to improve user satisfaction and purchase conversion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of the intelligent user behavior analysis platform based on big data and AI in this embodiment. DETAILED DESCRIPTION

[0038] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0039] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0040] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0041] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0042] See also Figure 1 As shown, it is a flow chart of the intelligent user behavior analysis platform based on big data and AI in this embodiment, including:

[0043] Data collection module, used to collect web page data and mobile terminal data;

[0044] A data preprocessing module, used for cleaning the web page data and the mobile terminal data to obtain valid web page data and valid mobile terminal data, and also for integrating the valid web page data and the valid mobile terminal data to obtain data to be converted, and also for converting the data to be converted into a user behavior data set, the data preprocessing module is connected to the data acquisition module;

[0045] A behavior association analysis module, used to judge the target item category according to the user behavior data set, and also used to calculate the target item association confidence according to the target item category and the target platform item set, and judge the user closeness according to the target item association confidence, and the behavior association analysis module is connected to the data preprocessing module;

[0046] A user behavior prediction module, used to judge the purchase tendency of the target platform item according to the target item association confidence, and the user behavior prediction module is connected to the behavior association analysis module;

[0047] The copy push selection module is used to judge the high-quality target item copy according to the purchase tendency of the target platform items, and is also used to judge the adaptation form of the high-quality target item copy according to the high-quality target item copy form confidence and the high-quality target item copy form support, and push the high-quality target item copy and the adaptation form as user selected copy, and is also used to compare the number of mobile terminal behaviors with the number of web terminal behaviors, and adjust the adaptation form determination process according to the comparison result. The push copy selection module is connected to the user behavior prediction module.

[0048] Specifically, the platform is applied to short video software. The platform comprehensively collects data from web pages and mobile terminals through the data collection module, ensuring the breadth and diversity of the data, and providing a solid foundation for subsequent data analysis and user behavior prediction. The platform data preprocessing module cleans and integrates the collected data, effectively removes noise and redundant information, and improves the accuracy and availability of the data. At the same time, the processed data is converted into a user behavior data set, providing direct and effective data support for subsequent behavior association analysis. Through the analysis of the user behavior data set by the behavior association analysis module, the support of the item category is calculated, and the target item category is accurately judged. Furthermore, the association confidence is calculated according to the target item category and the target platform item set, which effectively evaluates the closeness between the user and the item. In addition, the calculation order of the associated confidence is adjusted according to the length of the target item category, which optimizes the analysis process and improves the analysis efficiency. The user behavior prediction module calculates the target item promotion degree based on the results of the behavior association analysis and accurately predicts the user's item purchase tendency on the target platform. This step provides a strong basis for the subsequent copy push and helps to achieve precision marketing. The copy push selection module calculates the target item copy acceptance according to the user's purchase tendency and accurately judges the high-quality target item copy. At the same time, the promotion degree is calculated based on the copy form confidence and support, the copy adaptation form is optimized, and the high-quality target item copy and adaptation form are pushed as user selected copy, creating personalized copy design for customers, improving the pertinence and attractiveness of the copy, and helping to improve user satisfaction and purchase conversion rate.

[0049] Specifically, the web page data refers to various types of data generated, collected and stored on the web browser side, and the web page data includes the search behavior, browsing behavior, interactive behavior and purchasing behavior generated by the web browser side. The data collection module collects the web page data through JavaScript tracking code and Web log analysis. The mobile terminal data refers to various types of data generated, collected and stored by mobile phones and tablets. The mobile terminal data includes the search behavior, browsing behavior, interactive behavior and purchasing behavior generated by mobile phones and tablets. The data collection module collects the mobile terminal data through DK integration, Wi-Fi positioning and probe technology, and membership card and payment data association.

[0050] Specifically, the data preprocessing module includes a data cleaning unit, which performs noise removal on the web data and the mobile data, and performs missing value processing on the web data and the mobile data after the noise removal. After completing the missing value processing, the data in the web data and the mobile data that does not conform to the business logic is filtered to obtain valid web data and valid mobile data.

[0051] Specifically, the noise removal data processing refers to automatically identifying and filtering out duplicate records caused by network fluctuations and abnormal behavior data caused by user misoperation in web data and mobile data by setting threshold rules. The threshold rule refers to setting a limit for the data. When the data exceeds the limit, the data is considered abnormal and filtered out. The threshold rule includes a repetition frequency threshold. The repetition frequency threshold means that when the number of times a certain data appears repeatedly within a period of time exceeds this threshold, it is considered to be a duplicate record and should be filtered. The data that does not conform to business logic refers to records in e-commerce order data that have negative order amounts or zero item quantities.

[0052] Specifically, by removing noise data processing, it is possible to automatically identify and filter out duplicate records caused by network fluctuations and abnormal behavior data caused by user misoperation. Removing these noise data can significantly improve the quality of the data. Missing value processing is performed on the data after noise data removal, and missing data is filled in to make the data set more complete, which can ensure the accuracy and stability of data analysis. By filtering invalid data processing, data that does not conform to business logic can be excluded. After the above processing steps, the valid web data and valid mobile data obtained provide a solid foundation for subsequent data analysis.

[0053] Specifically, the data preprocessing module also includes a data integration unit, which performs data conflict elimination processing on the data in the valid web data and the valid mobile data to eliminate the conflicting data in the valid web data and the valid mobile data, and performs multi-source data fusion processing on the valid web data and the valid mobile data after the data conflict elimination processing to obtain the data to be converted.

[0054] Specifically, the data conflict elimination process refers to resolving conflicts by setting data update time priority when the same user has different classification labels for the same item in different systems, recording the last update time of the data, and giving priority to data with a later update time in case of conflict. The multi-source data fusion process refers to integrating user data from multiple different channels and systems, establishing a unified data relationship model, and clarifying the entities, attributes and relationships in the unified data relationship model. The data relationship model refers to a structure used to organize and represent user data and their relationships, which includes entities and their attributes, as well as relationships between entities.

[0055] Specifically, data conflict elimination processing can identify and correct conflicting data in valid web data and valid mobile data to ensure data accuracy and consistency. Multi-source data fusion processing can integrate valid web data and valid mobile data from different channels and systems to form a comprehensive data set, through which more valuable information can be mined to provide assistance for subsequent decision-making.

[0056] Specifically, the data preprocessing module also includes a data conversion unit, which discretizes various types of data in the data to be converted and standardizes the discretized data to be converted to obtain a user behavior data set, which includes basic user behaviors.

[0057] Specifically, the discretization processing refers to dividing the continuous user behavior data in the data to be converted into equal-width intervals and classifying the data to be converted. The standardization processing refers to converting the text data and web page addresses in the data to be converted into digital data corresponding to the user behavior type according to the item category.

[0058] Specifically, the data to be converted is classified through discretization processing, and the text data in the data to be converted is converted into digital data using standardization processing, so that the subsequent user behavior analysis module can use the data.

[0059] Specifically, the behavior association analysis module calculates the item category support Support{X} based on the item category behavior item set X and the total item behavior item set N in the user behavior data set, setting The item support Support{X} is compared with the preset minimum item support Support{X}0, and the target item category is determined according to the comparison result, where:

[0060] When Support{X}≥Support{X}0, the behavior association analysis module determines that the item category behavior item set X is a frequent item set, and uses the item category corresponding to the item category behavior item set X as the target item category;

[0061] When Support{X}<Support{X}0, the behavior association analysis module determines that the item category behavior item set X is a non-frequent item set;

[0062] The count{X} refers to the number of times the item category behavior item set X appears in the user behavior data set, and the preset minimum item support Support{X}0 refers to the preset support value for determining whether the item category behavior item set X is a frequent item set, and is set to 0.3<Support{X}0<1.

[0063] Specifically, the item category behavior item set X refers to the behavior item set made by users in the user behavior data set for a certain type of item, and the behavior item set includes the number of search behaviors, the number of browsing behaviors, the number of interactive behaviors, and the number of purchase behaviors. The total item behavior number item set N refers to the sum of the behavior item sets made by users in the user behavior data set for all categories of items. The frequent item set refers to the item category behavior item set whose item category support is greater than the preset minimum item support.

[0064] Specifically, by setting a preset minimum item support, frequent item sets can be quickly screened out, reducing unnecessary calculations, thereby improving the overall efficiency of data mining. By screening out frequent item sets, the foundation can be laid for subsequent confidence and lift calculations, thereby discovering more valuable association rules.

[0065] Specifically, the behavior association analysis module obtains the item category behavior item set of the target item category, takes it as the target item category behavior item set Xa, and calculates the target item association confidence according to the support Support(Xa∪Y) of the union of the target item category behavior item set Xa and the target platform item set Y set up Support(Xa∪Y) is the support of the target item category behavior item set Xa and the target platform item set Y. The behavior association analysis module converts the target item association confidence Minimum confidence level of the preset association rule A comparison is performed, and the user closeness is judged based on the comparison results, where:

[0066] when When , the behavior association analysis module determines that the user closeness is high;

[0067] when When , the behavior association analysis module determines that the user closeness is low;

[0068] The preset association rule minimum confidence Refers to the preset value of the association rule for judging the closeness of users.

[0069] Specifically, the target item category behavior item set Xa refers to the item category behavior item set of the same item category in the frequent item set corresponding to the target item category, the target platform item set Y refers to the sum of items corresponding to the target item category owned by the platform, the user closeness refers to the closeness between platform items and user preferences, that is, the degree of association between platform items and user preferences, and the support Support (Xa∪Y) of the union of the target item category behavior item set Xa and the target platform item set Y is calculated based on the target item category behavior item set Xa, the target platform item set Y, the total item behavior item set N and the platform item set Yx.

[0070] Specifically, by calculating the association confidence of the target item and combining it with the preset minimum confidence of the association rule, item set combinations with high user closeness can be screened out, thereby improving the accuracy of predicting future user behavior.

[0071] Specifically, the user behavior prediction module predicts the target item based on its associated confidence. And the target platform item support Support (Y) calculates the target item promotion degree, set And judge the purchase tendency of the target platform items based on the calculation results, where:

[0072] when When the user behavior prediction module determines that the target platform item has a high purchase tendency, a high-frequency copy recommendation is made for the target platform item;

[0073] when When the user behavior prediction module determines that the purchase tendency of the target platform item is average, a random copy recommendation is made for the target platform item;

[0074] when When the user behavior prediction module determines that the purchase tendency of the target platform item is low, no copy recommendation of the target platform item is performed.

[0075] Specifically, the target item improvement degree It is a key metric used to evaluate the strength of association between two item sets in association rules, namely, the target item category behavior item set Xa and the target platform item item set Y. The target platform item support is an indicator used to reflect the user's preference for the target platform item. The target platform item support (Y) is calculated based on the target platform item item set Y and the platform item total item set Yx.

[0076] Specifically, by calculating the lift degree of the target item, the user's purchasing desire can be determined, and different copywriting recommendation plans can be implemented for the user. When the lift degree is greater than 1, it is possible to identify users who have a high purchase intention for a specific product and make high-frequency copywriting recommendations to them, thereby improving the conversion rate of the copywriting and user satisfaction. When the lift degree is equal to 1, random frequency copywriting recommendations are used to test user reactions to different products, providing data support for subsequent personalized marketing strategies. When the lift degree is less than 1, it is possible to identify users who are not interested in a specific product, thereby avoiding pushing related copywriting to them, saving copywriting costs and reducing user dissatisfaction.

[0077] Specifically, the copy push selection module selects the copy according to the support degree Support(A) of the copy A with high purchase tendency of the target platform, the support degree Support(A∪B) of the union of copy A and target user B, and the user copy confidence. Calculate the target item copy acceptance ,set up And judge the high-quality target item copy based on the calculation results, among which:

[0078] when When the target copy is received, the copy push selection module determines that the target copy has a high acceptance rate, and the copy A is a high-quality target item copy, and increases the frequency of pushing the copy A to the user B;

[0079] when When the copy push selection module determines that the target copy has low acceptance, the copy A does not belong to the high-quality target item copy, and the copy A is not pushed to the target user B.

[0080] Specifically, the user copy confidence refers to the platform's evaluation of the strength of the association between the user and the copy. The high-quality target item copy refers to the copy with high user acceptance. The copy A support Support (A) is calculated based on the item set A of the copy corresponding to the target platform item and the total type item set Ax of the target platform copy. The support degree Support(A∪B) of the union of the document A and the target user B is calculated based on the union of the item set A and the target user B. Where Bx is the sum of all user types.

[0081] Specifically, by comprehensively considering the purchase tendency of items on the target platform, the support between the copy and the target users, and the user's copy confidence, the solution can more accurately determine which copy is more likely to be accepted and liked by the target users, helping to reduce irrelevant and unwelcome information push, thereby improving users' reading experience and satisfaction.

[0082] Specifically, the copy push selection module selects the copy of the high-quality target item based on its confidence level. And the support degree of the high-quality target item copywriting form Support (D) is used to calculate the improvement degree of the high-quality target item copywriting form set up , and judge the adaptation form of the high-quality target item copy based on the calculation results, where:

[0083] when When the copy push selection module determines that the adaptation form of the high-quality target item copy is appropriate, and pushes it;

[0084] when When the copy push selection module determines that the adaptation form of the high-quality target item copy is inappropriate, the push is cancelled.

[0085] Specifically, the high-quality target item copy form support is an indicator that reflects the user's support for the high-quality target item copy form, the high-quality target item copy form confidence is an indicator that reflects the degree of correlation between the user and the high-quality target item copy form, the high-quality target item copy form enhancement is a measure of the degree of advantage of a specific form of high-quality target item copy over other forms in attracting user attention, conveying product information and stimulating purchasing desire, the adaptation form refers to the formal adjustment and optimization of the high-quality target item copy according to the characteristics of the target user group, the characteristics of the target platform and the needs of item dissemination, so as to ensure that it can be presented to the user in the best state, the adaptation form includes the copy length, copy content and copy format, the high-quality target item copy form support Support (D) is calculated based on the high-quality target item copy form item set D and the total copy form item set Dx, and the setting Confidence of the high-quality target product copywriting form It is based on the support degree of the high-quality target item copywriting form Support (D), the support degree of the union of the high-quality target item copywriting form item set D and the target user B (D∪B), and sets The support degree Support (D∪B) of the union of the high-quality target item copywriting form item set D and the target user B is calculated based on the high-quality target item copywriting form item set D, the target user B, the sum item set Bx of all user types and the total copywriting form item set Dx.

[0086]

[0087] Specifically, by comprehensively considering the matching degree between the copy form and the target users and target platforms (i.e., the copy form support) as well as the reliability and effectiveness of the copy form itself (i.e., the copy form confidence), the solution can more accurately determine which copy form is most suitable for the target users, thereby improving the accuracy and effectiveness of the push.

[0088] Specifically, when the adaptation form is the copy length, the copy push selection module compares the number of mobile terminal behaviors P with the number of web page behaviors Q, and adjusts the adaptation form determination process according to the comparison result, wherein:

[0089] When P<Q, no adjustment is made to the adaptation form determination process;

[0090] When P≥Q, adjust the adaptation form determination process and set the adjustment coefficient V=0.6+e -(P-Q)-1 , e is the base of the natural logarithm, and the degree of improvement of the copywriting form of high-quality target items is based on the adjustment coefficient V After adjustment, the text form of high-quality target items has been improved to W.

[0091] Specifically, the number of mobile terminal behaviors refers to the number of mobile terminal behaviors on a certain category of items in the user behavior data set, and the number of web terminal behaviors refers to the number of web terminal behaviors on a certain category of items in the user behavior data set.

[0092] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An intelligent user behavior analysis platform based on big data and AI, characterized by: include: Data collection module, used to collect web page data and mobile terminal data; A data preprocessing module, for cleaning the web page data and the mobile terminal data to obtain valid web page data and valid mobile terminal data, for integrating the valid web page data and the valid mobile terminal data to obtain data to be converted, and for converting the data to be converted into a user behavior data set; A behavior association analysis module, used to judge the target item category according to the user behavior data set, and also used to calculate the target item association confidence according to the target item category and the target platform item set, and judge the user closeness according to the target item association confidence; A user behavior prediction module is used to judge the purchase tendency of the target platform items according to the association confidence of the target items; The copy push selection module is used to judge the high-quality target item copy according to the purchase tendency of the target platform items, and is also used to judge the adaptation form of the high-quality target item copy according to the high-quality target item copy form confidence and the high-quality target item copy form support, and push the high-quality target item copy and the adaptation form as user selected copy, and is also used to compare the number of mobile terminal behaviors with the number of web terminal behaviors, and adjust the adaptation form determination process according to the comparison result.

2. The intelligent user behavior analysis platform based on big data and AI according to claim 1 is characterized in that: The data preprocessing module includes a data cleaning unit, which performs noise removal on the web data and the mobile data, and performs missing value processing on the web data and the mobile data after the noise removal. After completing the missing value processing, the data in the web data and the mobile data that does not conform to the business logic is filtered to obtain valid web data and valid mobile data.

3. The intelligent user behavior analysis platform based on big data and AI according to claim 2 is characterized in that: The data preprocessing module also includes a data integration unit, which performs data conflict elimination processing on the data in the valid web data and the valid mobile data to eliminate the conflicting data in the valid web data and the valid mobile data, and performs multi-source data fusion processing on the valid web data and the valid mobile data after the data conflict elimination processing to obtain the data to be converted.

4. The intelligent user behavior analysis platform based on big data and AI according to claim 3 is characterized in that: The data preprocessing module also includes a data conversion unit, which discretizes various types of data in the data to be converted and standardizes the discretized data to be converted to obtain a user behavior data set, wherein the user behavior data set includes basic user behaviors.

5. The intelligent user behavior analysis platform based on big data and AI according to claim 4 is characterized in that: The behavior association analysis module calculates the item category support Support{X} based on the item category behavior item set X and the total item behavior item set N in the user behavior data set, setting The item support Support{X} is compared with the preset minimum item support Support{X}0, and the target item category is determined according to the comparison result, where: When Support{X}≥Support{X}0, the behavior association analysis module determines that the item category behavior item set X is a frequent item set, and uses the item category corresponding to the item category behavior item set X as the target item category; When Support{X}<Support{X}0, the behavior association analysis module determines that the item category behavior item set X is a non-frequent item set; The count{X} refers to the number of times the item category behavior item set X appears in the user behavior data set, and the preset minimum item support Support{X}0 refers to the preset support value for determining whether the item category behavior item set X is a frequent item set, and is set to 0.3<Support{X}0<1.

6. The intelligent user behavior analysis platform based on big data and AI according to claim 5 is characterized in that: The behavior association analysis module obtains the item category behavior item set of the target item category, takes it as the target item category behavior item set Xa, and calculates the target item association confidence according to the support Support(Xa∪Y) of the union of the target item category behavior item set Xa and the target platform item set Y set up Support(Xa∪Y) is the support of the target item category behavior item set Xa and the target platform item set Y. The behavior association analysis module converts the target item association confidence Minimum confidence level of the preset association rule A comparison is performed, and the user closeness is judged based on the comparison results, where: when When , the behavior association analysis module determines that the user closeness is high; when When , the behavior association analysis module determines that the user closeness is low; The preset association rule minimum confidence Refers to the preset value of the association rule for judging the closeness of users.

7. The intelligent user behavior analysis platform based on big data and AI according to claim 6 is characterized in that: The user behavior prediction module determines the confidence level of the target item association. And the target platform item support Support (Y) calculates the target item promotion degree, set And judge the purchase tendency of the target platform items based on the calculation results, where: when When the user behavior prediction module determines that the target platform item has a high purchase tendency, a high-frequency copy recommendation is made for the target platform item; when When the user behavior prediction module determines that the purchase tendency of the target platform item is average, a random copy recommendation is made for the target platform item; when When the user behavior prediction module determines that the purchase tendency of the target platform item is low, no copy recommendation of the target platform item is performed.

8. The intelligent user behavior analysis platform based on big data and AI according to claim 7 is characterized in that: The copy push selection module selects the copy according to the support degree Support(A) of the copy A with high purchase tendency of the target platform, the support degree Support(A∪B) of the union of copy A and target user B, and the user copy confidence Calculate the target item copy acceptance set up And judge the high-quality target item copy based on the calculation results, among which: when When the target copy is received, the copy push selection module determines that the target copy has a high acceptance rate, and the copy A is a high-quality target item copy, and increases the frequency of pushing the copy A to the user B; when When the copy push selection module determines that the target copy has low acceptance, the copy A does not belong to the high-quality target item copy, and the copy A is not pushed to the target user B.

9. The intelligent user behavior analysis platform based on big data and AI according to claim 8 is characterized in that: The copy push selection module selects the content based on the confidence level of the content of the high-quality target items. And the support degree of the high-quality target item copywriting form Support (D) is used to calculate the improvement degree of the high-quality target item copywriting form set up , and judge the adaptation form of the high-quality target item copy based on the calculation results, where: when When the copy push selection module determines that the adaptation form of the high-quality target item copy is appropriate, and pushes it; when When the copy push selection module determines that the adaptation form of the high-quality target item copy is inappropriate, the push is cancelled.

10. The intelligent user behavior analysis platform based on big data and AI according to claim 9, characterized in that: When the adaptation form is the copy length, the copy push selection module compares the number of mobile terminal behaviors P with the number of web page behaviors Q, and adjusts the adaptation form determination process according to the comparison result, wherein: When P<Q, no adjustment is made to the adaptation form determination process; When P≥Q, adjust the adaptation form determination process and set the adjustment coefficient V=0.6+e -(P-Q)-1 , e is the base of the natural logarithm, and the degree of improvement of the copywriting form of high-quality target items is based on the adjustment coefficient V After adjustment, the text form of high-quality target items has been improved to W.

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