Research And Application Of Point Of Interest Recommendation Algorithm Based On Context Awareness

In recent years,the development of mobile devices and sensing technology has led to the accumulation of point of interest data,and the development of perception devices makes context awareness recommendation system become one of the hot spots in the field of recommendation system research.In order to help users find the points of interest that meet the users' preferences in the current context from the mass data,the points of interest recommendation integrating the contextual information came into being.However,it is still faced with many contextual information and the sparsity of user-POI-context check-in matrix and so on.Therefore,this paper first considers the main contextual factors that affect the points of interest recommendation to be sorted out and classified,and then use effective methods to alleviate the sparsity of the checkin matrix and improve the accuracy of the recommendation results.The main work of this paper is as follows.(1)In theoretical research,aiming at the problem that affect the preference of users' points of interest,this paper improves the traditional classification of contextual factors.According to the results of the questionnaire,ten main contextual factor affecting the preference of users' points of interest are analyzed and determined.In order to solve the problem of the sparsity of user-POI-context check-in matrix,this paper calculates the user neighbor set based on collaborative filtering,LDA theme model and user context factor.Then,in the user neighbor,the improved scenario utility calculation method is used to measure the similarity of the user based on content.Finally,the mixed user similarity and context similarity realize the target user unknown resource recommendation.The experimental results on Yelp dataset and Foursquare dataset show that the points of interest recommendation algorithm based on context awareness has higher accuracy.(2)In the application example,based on the above research,this paper designs and implements a personalized travel recommendation system based on context information,which fully excavates the contextual factor of users,recommends the scenic spots suitable for the current context for users,and meets the needs of users for personalized tourism recommendation system.The system also verifies the importance of the improved algorithm in the practical application.There are 42 pictures,17 tables and 64 references in this paper.