| WeChat has become the most widely used social program in people's daily lives.WeChat applet has brought great changes to the traditional mobile application market.WeChat applet users can use the app without having to download the app,opening a "reach at your fingertips" mode.As more and more users use WeChat applets,it is important for the applet developer to analyze user behavior using applets as a basis for applet developers to optimize applets.This article analyzes the user behavior of WeChat applet applets developed by the project team.It mainly includes the following three aspects:discovering the rules of users using applet applets,user forwarding analysis,and mining users' interest in forum forums.Firstly,this paper chooses the k-medios algorithm which is suitable for the user behavior analysis of the applet applets,and makes corresponding improvements on the shortcomings of large initial randomness and iterative updating convergence slow.The comparative analysis of the clustering results before and after the improvement of the algorithm in the forum applet user behavior analysis and the usage behavior characteristics of the applet user in the forum are obtained.Later,using the applet's forwarding function,we analyzed the apples and appositons of WeChat applets and new user contributions from forwarding on WeChat applets.Finally,the user's browsing behavior in a section largely reflects his interest in this section of information.This article elaborates the method of collecting user behavior data in WeChat client,and on this basis,processes the basic user data and constructs a behavior model to calculate user interest.The user's basic browsing behavior data is converted to the forum dwell time,the number of clicks within the forum,the forum within the slide,the number of forum visits four analyzable behavior events.Based on the M5 model tree,user-oriented user behavior analysis model is constructed,and comparative analysis and evaluation of experimental results are carried out. |
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