Rights service management method, device and medium based on user behavior

Abstract

The present invention discloses a method, device and medium for managing rights and interests services based on user behavior, and relates to the field of data processing methods. The method includes: obtaining the first behavior information of the first user, and analyzing the first behavior information by calling a timing strategy to obtain the first user behavior timing, and extracting the first rights and interests behavior to obtain the corresponding first interaction time. The first interaction time is analyzed in combination with a predetermined interaction threshold constraint to obtain the first behavior time period. The first interaction behavior set corresponding to the time period is matched with the first behavior information, and clustered to obtain the first clustering result. The target clustering result is obtained by traversing the match in the user behavior database, and the corresponding target user is obtained. The first rights and interests service recommendation table is formed according to the corresponding target rights and interests behavior, and is displayed to the user terminal of the first user. The technical problems of insufficient recommendation accuracy and low efficiency of historical user matching in the prior art when managing rights and interests services based on user behavior are solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing methods, and in particular to a rights service management method, device and medium based on user behavior. Background Art

[0002] With the rapid development of internet services, user behavior analysis and personalized recommendation technologies have become core means of improving business conversion rates. Traditional recommendation systems often make recommendations based on static behavior tags or interaction data from a single point in time. They treat user behavior as discrete events and fail to effectively capture the temporal characteristics of related operations before and after equity behavior. This results in, for example, inaccurate identification of the prime conversion period after coupon redemption. This leads to an inaccurate grasp of the temporal characteristics of user behavior, affecting the accuracy of recommendations. Furthermore, cluster analysis of user behavior makes it difficult to efficiently match target users with similar behavior patterns to the current user within large amounts of historical user data, which in turn affects the efficiency of obtaining recommendation results.

[0003] Therefore, when rights services are managed based on user behavior in the existing technology, there are technical problems such as insufficient recommendation accuracy and low efficiency in historical user matching. Summary of the Invention

[0004] This application addresses the existing technical issues of insufficient recommendation accuracy and inefficient historical user matching in user behavior-based rights service management by providing a method, device, and medium for rights service management based on user behavior. Through precise timing strategies, effective vectorized analysis and similarity calculation, and a dynamic resource scheduling mechanism, the accuracy of rights service recommendations, matching efficiency, and user experience are improved.

[0005] The present application provides a rights service management method based on user behavior, which includes: obtaining first behavior information of a first user, and calling a timing strategy to analyze the first behavior information to obtain a first user behavior time sequence; extracting a first rights behavior in the first user behavior time sequence, and obtaining a first interaction time corresponding to the first rights behavior; analyzing the first interaction time in combination with a predetermined interaction threshold constraint to obtain a first behavior time period; matching a first interaction behavior set corresponding to the first behavior time period in the first behavior information, and performing cluster analysis on the first interaction behavior set to obtain a first clustering result; traversing and matching the first clustering result in a user behavior database to obtain a target clustering result, and obtaining the corresponding target user; establishing a first rights service recommendation table based on the target rights behavior of the target user, and displaying the first rights service recommendation table to the user terminal of the first user.

[0006] In the implementation method, the standard traffic multi-dimensional operation data flow is obtained, including: obtaining the first behavior information of the first user, and calling the timing strategy to analyze the first behavior information to obtain the first user behavior timing, including: extracting any rights and interests behavior in the first behavior information according to the timing strategy; obtaining the time when the first user performs the arbitrary rights and interests behavior, recorded as arbitrary interaction time; and constructing the first user behavior timing according to the correspondence between the arbitrary rights and interests behavior and the arbitrary interaction time.

[0007] In an implementation, the first interaction time is analyzed according to the predetermined pre-interaction step threshold and the predetermined post-interaction step threshold in the predetermined interaction threshold constraint to obtain the first behavior time period.

[0008] In the implementation method, the first clustering result is traversed and matched in the user behavior database to obtain a target clustering result, and the corresponding target user is obtained, including: according to the vectorization strategy, vectorizing and analyzing the multiple behavior clusters with behavior count identifiers in the first clustering result to generate a first behavior vector; extracting the first behavior data group from the user behavior database, the first behavior data group refers to the first historical behavior clustering result and the first historical equity behavior of the first historical user; obtaining the first historical behavior vector of the first historical behavior clustering result according to the vectorization strategy; calculating the first similarity between the first behavior vector and the first historical behavior vector; if the first similarity reaches a predetermined similarity threshold, using the first historical behavior clustering result as the target clustering result, and recording the first historical user as the target user.

[0009] In the implementation method, according to the vectorization strategy, a vectorization analysis is performed on multiple behavior clusters with identifications of behavior times in the first clustering result to generate a first behavior vector, including: extracting the first behavior cluster from the multiple behavior clusters with identifications of behavior times, and obtaining the first behavior frequency of the first behavior cluster; obtaining the first vector ordinal of the first behavior cluster according to the vectorization strategy; marking the first behavior frequency to the first vector ordinal to obtain the first behavior vector.

[0010] In an implementation, calculating the first similarity between the first behavior vector and the first historical behavior vector includes: obtaining an arbitrary vector ordinal number; matching the second behavior frequency and the third behavior frequency corresponding to the arbitrary vector ordinal number in the first behavior vector and the first historical behavior vector in turn; obtaining the similarity between the second behavior frequency and the third behavior frequency; and obtaining the first similarity based on the similarity.

[0011] In the implementation method, after obtaining the similarity between the second behavior frequency and the third behavior frequency, it also includes: obtaining an arbitrary behavior cluster corresponding to the arbitrary vector ordinal number; performing a collaborative analysis on the arbitrary behavior cluster and the target equity behavior to obtain the arbitrary cluster information value of the arbitrary behavior cluster; and adjusting the similarity with the arbitrary cluster information value as a weight.

[0012] In the implementation method, after forming a first equity service recommendation table based on the target equity behavior of the target user and displaying the first equity service recommendation table to the user terminal of the first user, it also includes: matching the target service channel with the target equity behavior; load monitoring the target service channel to obtain the target load rate; and dynamically scheduling resources for the target service channel based on the target load rate.

[0013] The present application also provides an electronic device, comprising: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the user behavior-based rights service management method provided in the present application.

[0014] The present application provides a computer-readable storage medium storing a computer program. When the program is executed by a processor, the method for managing rights and interests services based on user behavior provided by the present application is implemented.

[0015] The user behavior-based rights service management method, device, and medium proposed in this application are to obtain the first user's first behavior information and analyze the first behavior information using a timing strategy to obtain the first user behavior time sequence; extract the first rights behavior in the first user behavior time sequence and obtain the first interaction time corresponding to the first rights behavior; analyze the first interaction time in combination with a predetermined interaction threshold constraint to obtain the first behavior time period; match the first interaction behavior set corresponding to the first behavior time period in the first behavior information, and perform cluster analysis on the first interaction behavior set to obtain a first clustering result; traverse and match the first clustering result in the user behavior database to obtain a target clustering result and obtain the corresponding target user; establish a first rights service recommendation table based on the target rights behavior of the target user, and display the first rights service recommendation table to the user terminal of the first user. This solves the technical problems of insufficient recommendation accuracy and low efficiency of historical user matching in the existing technology when rights service management is based on user behavior. Through precise timing strategies, effective vector analysis and similarity calculation, and dynamic resource scheduling mechanisms, the accuracy, matching efficiency, and user experience of rights service recommendations are improved.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0018] Figure 1 A flowchart of a user behavior-based rights service management method provided in an embodiment of the present application;

[0019] Figure 2 A flowchart of obtaining corresponding target users for the rights service management method based on user behavior provided in an embodiment of the present application;

[0020] Figure 3 A structural diagram of an electronic device corresponding to the user behavior-based rights service management method provided in an embodiment of the present application.

[0021] Description of reference numerals: processor 31 , memory 32 , input device 33 , output device 34 . DETAILED DESCRIPTION

[0022] Example 1

[0023] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0024] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0025] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0026] like Figure 1 As shown, the embodiment of the present application provides a rights service management method based on user behavior, the method comprising:

[0027] Obtain the first behavior information of the first user, and call the timing strategy to analyze the first behavior information to obtain the first user behavior time sequence; extract the first equity behavior in the first user behavior time sequence, and obtain the first interaction time corresponding to the first equity behavior; analyze the first interaction time in combination with the predetermined interaction threshold constraint to obtain the first behavior time period.

[0028] Using tracking technology or a log collection system, all interactions and benefits of a first user on the platform are collected to obtain the first user's first behavior information. Interactions include page browsing, button clicks, and order payments, and benefits include red envelope redemption and cash benefits withdrawal. The timing strategy involves obtaining the benefit object and execution time from the first behavior information, and based on the corresponding relationship between the two, obtaining the first user's behavior time series. For example, if user A receives a coupon at 09:05, then receiving the coupon is a benefits behavior, executed at 09:05. Subsequently, the first benefits behavior in the first user behavior time series is extracted. The first benefits behavior is a random benefits behavior in the first user behavior time series. The first interaction time corresponding to the first benefits behavior is also obtained. Furthermore, the first interaction time is analyzed in conjunction with a predetermined interaction threshold constraint. The predetermined interaction threshold constraint is a system-defined time range rule that captures associated operations before and after the benefits behavior, including a pre-prescribed interaction step threshold and a post-prescribed interaction step threshold. The pre-prescribed interaction step threshold and the post-prescribed interaction step threshold are used to capture the time period before and after the first interaction time, respectively, to obtain the first behavior time period. Taking the first interaction time 09:05 as an example, the predetermined interaction threshold constraint is "30 minutes before and after the rights and interests behavior", and the behavior period of user A is from 08:35 to 09:35.

[0029] The method provided in the embodiment of the present application also includes: extracting any rights and interests behavior in the first behavior information according to the timing strategy; obtaining the time when the first user performs the arbitrary rights and interests behavior, recorded as arbitrary interaction time; and constructing the first user behavior timing according to the corresponding relationship between the arbitrary rights and interests behavior and the arbitrary interaction time.

[0030] The first behavior information of the first user is obtained, and the timing strategy is called to analyze the first behavior information to obtain the first user behavior sequence, including: extracting any rights and interests behavior in the first behavior information according to the timing strategy. The rights and interests behavior has different meanings according to different business scenarios. In the e-commerce scenario, operations such as "coupon collection", "points redemption", and "member service activation" are all rights and interests behaviors. Specific rights and interests behaviors can be set accordingly according to the actual application scenario. Subsequently, the time when the first user executes the rights, that is, uses or obtains any rights and interests behavior, is obtained, and the time of using or obtaining any rights and interests behavior is recorded as any interaction time. Further, the corresponding relationship between the any rights and interests behavior and the any interaction time is used to construct the first user behavior sequence, that is, based on the corresponding relationship between any rights and interests behavior and the corresponding usage time, the behavior sequence of the first user is constructed to obtain the first user behavior sequence. Each right and the corresponding execution time are recorded in the first user behavior sequence.

[0031] Match the first interaction behavior set corresponding to the first behavior time period in the first behavior information, and perform cluster analysis on the first interaction behavior set to obtain a first clustering result; traverse and match the first clustering result in the user behavior database to obtain a target clustering result, and obtain the corresponding target user; establish a first rights and interests service recommendation table based on the target rights and interests behavior of the target user, and display the first rights and interests service recommendation table to the user terminal of the first user.

[0032] The first behavior information is matched with a first interactive behavior set corresponding to the first behavior time period. The interactive behavior set includes all non-rights-based interactive operations, such as page browsing, button clicks, purchases, and favorites. Cluster analysis is then performed on the interactive behaviors in the first interactive behavior set, such as clustering all clicks, browsing, purchases, and favorites into corresponding behavior clusters, thereby obtaining a first clustering result. Furthermore, the first clustering result is traversed and matched against a user behavior database to obtain a target clustering result. The target clustering result is a result whose similarity with the first clustering result is greater than or equal to a predetermined similarity threshold, thereby obtaining a target user corresponding to the target clustering result. Finally, a first rights service recommendation table is constructed based on the target rights-based behaviors recorded in the target user's history, and the first rights service recommendation table is displayed to the user terminal of the first user. This solves the technical problems of insufficient recommendation accuracy and low historical user matching efficiency in the prior art of rights service management based on user behavior. Through precise timing strategies, effective vectorized analysis and similarity calculation, and a dynamic resource scheduling mechanism, the accuracy, matching efficiency, and user experience of rights service recommendations are improved.

[0033] like Figure 2 As shown, the method provided in the embodiment of the present application also includes: according to the vectorization strategy, performing vectorization analysis on multiple behavior clusters with behavior number identifications in the first clustering result to generate a first behavior vector; extracting the first behavior data group in the user behavior database, the first behavior data group refers to the first historical behavior clustering result and the first historical equity behavior of the first historical user; obtaining the first historical behavior vector of the first historical behavior clustering result according to the vectorization strategy; calculating the first similarity between the first behavior vector and the first historical behavior vector; if the first similarity reaches a predetermined similarity threshold, using the first historical behavior clustering result as the target clustering result, and recording the first historical user as the target user.

[0034] According to the vectorization strategy, a vectorization analysis is performed on multiple behavior clusters with identification of the number of behaviors in the first clustering result to generate a first behavior vector. The vectorization strategy is a strategy for vectorizing each behavior cluster in the first clustering result. By vectorizing each cluster cluster in the first clustering result, subsequent analysis can directly perform data processing based on vector features, thereby improving the efficiency of data analysis. Subsequently, the first behavior data group in the user behavior database is extracted. The user behavior database records the data recorded by various types of users in the historical system or software, including the user and the historical behavior clustering results and historical rights and interests behaviors of the user in the corresponding behavior time period. Among them, the first behavior data group refers to the first historical behavior clustering result and the first historical rights and interests behavior of the first historical user. Further, according to the vectorization strategy, the same vectorization method is adopted to obtain the first historical behavior vector corresponding to the first historical behavior clustering result of the first historical user. The first historical behavior vector contains multiple vectors after the behavior vectorization. Further, the first similarity between the first behavior vector and the first historical behavior vector is calculated. Finally, through a pre-set predetermined similarity threshold, it is determined whether the first similarity is greater than or equal to the predetermined similarity threshold. If the first similarity reaches or is greater than or equal to the predetermined similarity threshold, the first historical behavior clustering result is used as the target clustering result, and the first historical user is recorded as the target user.

[0035] The method provided in an embodiment of the present application also includes: extracting a first behavior cluster from multiple behavior clusters with behavior count identifiers, and obtaining a first behavior frequency of the first behavior cluster; obtaining a first vector ordinal number of the first behavior cluster according to the vectorization strategy; marking the first behavior frequency to the first vector ordinal number to obtain the first behavior vector.

[0036] According to the vectorization strategy, a vectorization analysis is performed on multiple behavior clusters with behavior count identifiers in the first clustering result to generate a first behavior vector, including: extracting the first behavior cluster from the multiple behavior clusters with behavior count identifiers in the first clustering result, the first behavior cluster is a random behavior cluster with a behavior count identifier, and the behavior count identifier is the number of times the behavior appears in the behavior cluster, and at the same time obtaining the first behavior frequency of the first behavior cluster, the first behavior frequency is the specific behavior count of the first behavior cluster. Further, according to the vectorization strategy, the first vector ordinal of the first behavior cluster is obtained, the first vector ordinal is the sequence identifier of the first behavior cluster in the first clustering result, which is used to reflect the specific behavior referred to by the first behavior cluster, and the sequence identifier is a fixed identifier and will not change due to the number of behavior clusters. Finally, the first behavior frequency is marked to the first vector ordinal to obtain the first behavior vector. Among them, the first behavior vector can contain multiple vectors after behavior vectorization. The first behavior vector thus obtained can clearly reflect the user's specific behavior and the corresponding frequency characteristics.

[0037] The method provided in an embodiment of the present application also includes: obtaining an arbitrary vector ordinal number; matching the second behavior frequency and the third behavior frequency corresponding to the arbitrary vector ordinal number in the first behavior vector and the first historical behavior vector in sequence; obtaining the similarity between the second behavior frequency and the third behavior frequency; and obtaining the first similarity based on the similarity.

[0038] Calculating the first similarity between the first behavior vector and the first historical behavior vector includes: obtaining an arbitrary vector ordinal, where the arbitrary vector ordinal is any one of the vector ordinals contained in the first behavior vector. Subsequently, matching the second behavior frequency and the third behavior frequency corresponding to the arbitrary vector ordinal in the first behavior vector and the first historical behavior vector respectively. The second behavior frequency is the behavior frequency corresponding to the arbitrary vector ordinal of the first behavior vector, and the third behavior frequency is the behavior frequency corresponding to the arbitrary vector ordinal of the first historical behavior vector. Further, obtaining the similarity between the second behavior frequency and the third behavior frequency, and obtaining the mean based on the similarity of all behavior clusters to obtain the first similarity. The first similarity reflects the similarity between the target user and the first user in the user behavior database.

[0039] The method provided in an embodiment of the present application further includes: obtaining an arbitrary behavior cluster corresponding to the arbitrary vector ordinal number; performing a collaborative analysis on the arbitrary behavior cluster and the target equity behavior to obtain an arbitrary cluster information value of the arbitrary behavior cluster; and adjusting the similarity using the arbitrary cluster information value as a weight.

[0040] After obtaining the similarity between the second behavior frequency and the third behavior frequency, the method further includes: obtaining the arbitrary behavior cluster corresponding to the arbitrary vector ordinal number, that is, obtaining the behavior corresponding to the arbitrary vector ordinal number. Furthermore, a collaborative analysis is performed on the arbitrary behavior cluster and the target equity behavior to obtain the arbitrary cluster information value of the arbitrary behavior cluster. This information value is the correlation between the behavior cluster and the target equity behavior. The higher the correlation, the higher the connection between the corresponding behavior and the target equity behavior. When performing the collaborative analysis, the number of records of the target equity behavior in all behavior data groups in the user behavior database is obtained, and all historical behavior clustering results in the behavior data groups containing the target equity behavior are included in advance. The number of occurrences of the behavior cluster in all historical behavior clustering results is obtained respectively. The information value of the behavior cluster is calculated based on the ratio of the number of occurrences to the number of records, and the corresponding arbitrary cluster information value is obtained. Finally, the similarity is adjusted using the arbitrary cluster information value as a weight. That is, the calculation result is multiplied by the weight parameter based on the original similarity to obtain the adjusted similarity.

[0041] The method provided in the embodiment of the present application also includes: matching the target service channel of the target equity behavior; monitoring the load of the target service channel to obtain a target load rate; and dynamically scheduling resources for the target service channel based on the target load rate.

[0042] After forming a first rights service recommendation table according to the target rights behavior of the target user and displaying the first rights service recommendation table to the user terminal of the first user, it also includes: matching the target rights behavior with the target service channel, the target service channel is a background interface or service that provides rights services, such as a member activation API, a coupon redemption interface, and is usually identified by a URL or service name. Subsequently, the target service channel is load monitored, and the target load rate is obtained by monitoring the current request volume of the service channel as a percentage of its maximum carrying capacity. For example, during the Double 11 period of a certain e-commerce platform, the QPS of the coupon redemption interface suddenly increased from 200 to 1500 (maximum capacity = 1200), and the load rate = 125%, triggering an overload alarm. Finally, the target service channel is dynamically scheduled for resources based on the target load rate.

[0043] The technical solution provided by the embodiment of the present invention obtains the first behavior information of the first user, and calls the timing strategy to analyze the first behavior information, obtains the first user behavior timing, extracts the first rights behavior, and obtains the corresponding first interaction time. The first interaction time is analyzed in combination with the predetermined interaction threshold constraint to obtain the first behavior time period. The first interaction behavior set corresponding to the time period is matched with the first behavior information, and clustered to obtain the first clustering result. The target clustering result is obtained by traversing the matching in the user behavior database, and the corresponding target user is obtained. The first rights service recommendation table is formed according to the corresponding target rights behavior, and displayed to the user terminal of the first user. The technical problems of insufficient recommendation accuracy and low efficiency of historical user matching in the existing technology when rights service management is based on user behavior are solved. Through precise timing strategies, effective vector analysis and similarity calculation, and dynamic resource scheduling mechanism, the accuracy, matching efficiency and user experience of rights service recommendations are improved.

[0044] Example 2

[0045] Based on the same inventive concept as the user behavior-based rights service management method in the aforementioned embodiment, the present invention further provides a user behavior-based rights service management system. The system can be implemented in hardware and / or software and can generally be integrated into an electronic device for executing the method provided in any embodiment of the present invention. The system includes: a behavior time series acquisition module for acquiring first behavior information of a first user and analyzing the first behavior information using a time series strategy to obtain a first user behavior time series; an interaction time acquisition module for extracting a first rights behavior from the first user behavior time series and obtaining a first interaction time corresponding to the first rights behavior; a behavior time period acquisition module for analyzing the first interaction time in combination with a predetermined interaction threshold constraint to obtain a first behavior time period; a clustering result acquisition module for matching a first interaction behavior set corresponding to the first behavior time period in the first behavior information and performing cluster analysis on the first interaction behavior set to obtain a first clustering result; a target user acquisition module for traversing and matching the first clustering result in a user behavior database to obtain a target clustering result and obtain the corresponding target user; and a recommendation display module for constructing a first rights service recommendation table based on the target rights behavior of the target user and displaying the first rights service recommendation table to the user terminal of the first user.

[0046] The specific configuration of the behavior timing acquisition module will be described in detail below. The behavior timing acquisition module may further include: obtaining the standard traffic multi-dimensional operation data stream, including: obtaining the first behavior information of the first user, and calling the timing strategy to analyze the first behavior information to obtain the first user behavior timing, including: extracting any equity behavior in the first behavior information according to the timing strategy; obtaining the time when the first user performs the arbitrary equity behavior, recorded as the arbitrary interaction time; and constructing the first user behavior timing based on the correspondence between the arbitrary equity behavior and the arbitrary interaction time.

[0047] The specific configuration of the behavior period acquisition module will be described in detail below. The behavior period acquisition module may further include: analyzing the first interaction time according to the predetermined pre-interaction step threshold and the predetermined post-interaction step threshold in the predetermined interaction threshold constraint to obtain the first behavior period.

[0048] The specific configuration of the clustering result acquisition module will be described in detail below. The clustering result acquisition module further includes: traversing and matching the first clustering result in the user behavior database to obtain the target clustering result, and obtaining the corresponding target user, including: according to the vectorization strategy, performing vectorization analysis on the multiple behavior clusters with behavior count identifiers in the first clustering result to generate a first behavior vector; extracting the first behavior data group in the user behavior database, the first behavior data group refers to the first historical behavior clustering result and the first historical equity behavior of the first historical user; obtaining the first historical behavior vector of the first historical behavior clustering result according to the vectorization strategy; calculating the first similarity between the first behavior vector and the first historical behavior vector; if the first similarity reaches a predetermined similarity threshold, the first historical behavior clustering result is used as the target clustering result, and the first historical user is recorded as the target user.

[0049] The specific configuration of the clustering result acquisition module will be described in detail below. The clustering result acquisition module further includes: performing vectorized analysis on multiple behavior clusters with behavior count identifiers in the first clustering result according to a vectorization strategy to generate a first behavior vector, including: extracting a first behavior cluster from the multiple behavior clusters with behavior count identifiers and obtaining a first behavior frequency of the first behavior cluster; obtaining a first vector ordinal number of the first behavior cluster according to the vectorization strategy; and marking the first behavior frequency to the first vector ordinal number to obtain the first behavior vector.

[0050] The specific configuration of the clustering result acquisition module will be described in detail below. The clustering result acquisition module further includes: calculating a first similarity between the first behavior vector and the first historical behavior vector, including: obtaining an arbitrary vector ordinal number; sequentially matching the second behavior frequency and third behavior frequency corresponding to the arbitrary vector ordinal number in the first behavior vector and the first historical behavior vector; obtaining the similarity between the second behavior frequency and the third behavior frequency; and obtaining the first similarity based on the similarity.

[0051] The specific configuration of the clustering result acquisition module will be described in detail below. The clustering result acquisition module further includes: after obtaining the similarity between the second behavior frequency and the third behavior frequency, it also includes: obtaining any behavior cluster corresponding to the arbitrary vector ordinal number; performing a collaborative analysis on the arbitrary behavior cluster and the target equity behavior to obtain the arbitrary cluster information value of the arbitrary behavior cluster; and adjusting the similarity using the arbitrary cluster information value as a weight.

[0052] The specific configuration of the recommendation display module will be described in detail below. The recommendation display module further includes: after forming a first equity service recommendation table based on the target equity behavior of the target user and displaying the first equity service recommendation table to the user terminal of the first user, it also includes: matching the target service channel with the target equity behavior; load monitoring the target service channel to obtain a target load rate; and dynamically scheduling resources for the target service channel based on the target load rate.

[0053] The various units and modules included are divided only according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0054] Example 3

[0055] Figure 3 This is a structural diagram of an electronic device provided in accordance with a third embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 3 As shown, the electronic device includes a processor 31, a memory 32, an input device 33 and an output device 34; the number of processors 31 in the electronic device can be one or more. Figure 3 Taking a processor 31 as an example, the processor 31, memory 32, input device 33 and output device 34 in the electronic device can be connected through a bus or other means. Figure 3 The bus connection is taken as an example.

[0056] Memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the user behavior-based rights and interests service management method in the embodiments of the present invention. Processor 31 executes the software programs, instructions, and modules stored in memory 32 to execute various functional applications and data processing of the computer device, thereby implementing the aforementioned user behavior-based rights and interests service management method.

[0057] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A rights service management method based on user behavior, characterized in that: include: Obtaining first behavior information of a first user, and invoking a time sequence strategy to analyze the first behavior information to obtain a time sequence of the first user behavior; Extracting a first equity behavior from the first user behavior time series, and obtaining a first interaction time corresponding to the first equity behavior; Analyzing the first interaction time in combination with a predetermined interaction threshold constraint to obtain a first behavior time period, wherein the predetermined interaction threshold constraint is a time range rule set by the system, including a pre-scheduled interaction step threshold and a post-scheduled interaction step threshold; matching a first interaction behavior set corresponding to the first behavior time period in the first behavior information, and performing cluster analysis on the first interaction behavior set to obtain a first clustering result; Traversing and matching the first clustering result in the user behavior database to obtain a target clustering result, and obtaining the corresponding target user; Creating a first rights and interests service recommendation table based on the target rights and interests behavior of the target user, and displaying the first rights and interests service recommendation table to the user terminal of the first user; Traversing and matching the first clustering result in the user behavior database to obtain a target clustering result, and obtaining the corresponding target user, including: Performing vector analysis on a plurality of behavior clusters with identifications of behavior times in the first clustering result according to a vectorization strategy to generate a first behavior vector; Extracting a first behavior data group from the user behavior database, where the first behavior data group refers to a first historical behavior clustering result and a first historical equity behavior of a first historical user; Obtaining a first historical behavior vector of the first historical behavior clustering result according to the vectorization strategy; Calculating a first similarity between the first behavior vector and the first historical behavior vector; If the first similarity reaches a predetermined similarity threshold, the first historical behavior clustering result is used as the target clustering result, and the first historical user is recorded as the target user.

2. The rights service management method based on user behavior according to claim 1 is characterized in that: Obtaining first behavior information of a first user, and invoking a time series strategy to analyze the first behavior information to obtain a time series of the first user behavior, including: Extracting any equity behavior from the first behavior information according to the timing strategy; Obtain the time when the first user performs the arbitrary rights and interests behavior, which is recorded as the arbitrary interaction time; The first user behavior time series is constructed according to the corresponding relationship between the arbitrary equity behavior and the arbitrary interaction time.

3. The rights service management method based on user behavior according to claim 1 is characterized in that: include: The first interaction time is analyzed according to the predetermined pre-interaction step threshold and the predetermined post-interaction step threshold in the predetermined interaction threshold constraint to obtain the first behavior time period.

4. The rights service management method based on user behavior according to claim 1 is characterized in that: According to the vectorization strategy, a vectorization analysis is performed on a plurality of behavior clusters with identifications of behavior times in the first clustering result to generate a first behavior vector, including: Extracting a first behavior cluster from a plurality of behavior clusters having identifications of behavior counts, and obtaining a first behavior frequency of the first behavior cluster; Obtaining a first vector ordinal number of the first behavior cluster according to the vectorization strategy; The frequency of the first behavior is labeled to the first vector ordinal number to obtain the first behavior vector.

5. The rights service management method based on user behavior according to claim 4 is characterized in that: Calculating a first similarity between the first behavior vector and the first historical behavior vector includes: Get the ordinal number of any vector; Sequentially matching the second behavior frequency and the third behavior frequency corresponding to the arbitrary vector ordinal number in the first behavior vector and the first historical behavior vector; Obtaining a similarity between the second behavior frequency and the third behavior frequency; The first similarity is obtained based on the similarity.

6. The rights service management method based on user behavior according to claim 5 is characterized in that: After obtaining the similarity between the second behavior frequency and the third behavior frequency, the method further includes: Obtaining any behavior cluster corresponding to the arbitrary vector ordinal number; Performing a collaborative analysis of the arbitrary behavior cluster and the target equity behavior to obtain the arbitrary cluster information value of the arbitrary behavior cluster, specifically obtaining the number of records of the target equity behavior in all behavior data groups in the user behavior database, and extracting all historical behavior clustering results in the behavior data groups that include the target equity behavior; obtaining the number of occurrences of the behavior cluster in all historical behavior clustering results; calculating the information value of the behavior cluster based on the ratio of the number of occurrences to the number of records, and obtaining the arbitrary cluster information value of the arbitrary behavior cluster; The similarity is adjusted using the information value of the arbitrary cluster as a weight.

7. The rights service management method based on user behavior according to claim 1 is characterized in that: After forming a first rights and interests service recommendation table according to the target rights and interests behavior of the target user and displaying the first rights and interests service recommendation table to the user terminal of the first user, the method further includes: A target service channel that matches the target equity behavior; Performing load monitoring on the target service channel to obtain a target load rate; Dynamic resource scheduling is performed on the target service channel based on the target load rate.

8. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the user behavior-based rights service management method according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for managing rights and interests services based on user behavior as described in any one of claims 1 to 7 is implemented.

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