Data Mining And Research Of Implicit User Behavior In Smart Campus

With the rapid development of information technology education,smart campus construction has become one of the key work of the school.Users of smart campus platform will produce a large number of implicit user behavior data which can reflect from the side of the individual's behavioral characteristics,hobbies and other potential information.So,using the implicit user behavior data for data mining and research which can have a positive effect for constructing the smart campus construction and serving the users of smart campus.Based on the related technology of data mining,this paper makes an individual recommendation and improvement of product design and product operation strategy based on implicit user behavior data analysis.This paper focuses on improving the relevant recommendation algorithm,to achieve the learning resources personalized recommendation for the users of smart campus platform.The specific work of the paper is as follows:The first work of this article:In order to research the learning resource web page click stream data of smart campus platform users,user's interest was mined and the JMATRIX algorithm was proposed to recommend personalized learning resources for them.At first,setting up the directed-graph model of user's resources click data based on the user's historical resources click stream datas,proposed and transformed the directed-graph model into matrix model to store.Then,obtaining the similarity of users by solving the similarity of matrix model,discarding the traditional way of solving the similarity of resources clicking paths and clicking frequency,and improved the efficiency and accuracy of the user's similarity.According to JMATRIX recommendation algorithm to obtain user similarity for user clustering,and personalized learning resources for similar users.Experimental results show that the JMATRIX algorithm has higher efficiency and more accurate recommendation effect compared to original algorithms.The second work of this paper:Based on smart campus product implicit user behavior data mining,to optimize product design and improve product operation strategy,then increase product economic efficiency.First of all,based on the function menu click frequency and click path data analysis of smart campus product,obtaining the user's product habits and optimizing the function of the menu page layout,to improve product user experience.Then based on the implicit user behavior data analysis to optimize the product operation strategy,at the same time,optimize product operations by analyzing the product of the operating indicators.While digging out the core users of the platform,and achieve effective protection for the core users of platform,which can improve the competitiveness of the platform.The research results in this paper have a certain contribution in the field of user personalized recommendation and optimization of product design and product operation.