Research On Personalized Recommendation Algorithm For Microblog

With the popularity and wide application of social media,the explosion of social network information such as microblog has highlighted the problem of information overload.In the face of massive data,it is difficult for users to find interesting information.The recommendation system is an important tool to solve this problem,and has received extensive attention from academia and industry.However,the existing microblog recommendation algorithms still have problems such as insufficient timeliness of interaction behavior,low recommendation accuracy under data sparse conditions,and hard group division.In view of the above problems,based on the analysis of the existing microblog recommendation algorithm,this paper improves the microblog recommendation algorithm based on social relationship and the microblog recommendation algorithm based on group recommendation.The main research contents of this paper include the following three aspects:(1)A microblog recommendation algorithm based on joint probability matrix decomposition.Aiming at the problem of insufficient timeliness of interaction behavior,based on the interaction behaviors such as praise,forwarding and comment between users,this paper proposes an influence evaluation model that can treat different interactions in different time periods by introducing the forgetting function.LTIM;For the problem of low recommendation accuracy under data sparse conditions,the joint probability matrix decomposition method is introduced to jointly decompose the user similarity matrix and the influence matrix,which alleviates the problem of low recommendation accuracy under data sparse conditions.(2)A microblog recommendation algorithm based on topological potential.For the problem of hard partitioning of groups,this paper uses LDA topic model to analyze the topic of microblog text,and constructs user interest preference vector with user-defined tags,uses KL distance to calculate the similarity distance between user preference vectors,and introduces topological potential.Users are clustered according to the preference similarity distance between users,and user groups are divided to realize user overlapping group division.(3)Designed and implemented the Weibo recommendation system.In this paper,the microblog recommendation algorithm based on social relationship and the group-based microblog recommendation algorithm are applied to the microblog recommendation system.The system includes a data source module,a preprocessing module,and a microblog recommendation module.The main function of the data source module is to crawl the microblog data.The preprocessing module mainly cleans and classifies the crawled data.The microblog recommendation module implements two recommendation algorithms to recommend content that matches the interests of the user.