Application Of Recommendation Algorithm In Social Media Platform

With the arising of social media platforms such as Douyin,Douban,Weibo,etc.,how to solve the "information overload" problem and recommend content more accurately on these platforms became an important topic of personalized recommendation.The traditional collaborative filtering recommendation algorithm only calculates the similarity of user ratings,and has serious cold start problems and sparseness problems.On the one hand,users' social relations can help us better explore the mutual relationship between them;on the other hand,the introduction of "users-tags" information is conducive to tapping the potential of users Interest preference,ease the sparsity of the algorithm.Based on the above analysis,this paper is to improve the deficiencies of traditional algorithms and improve the accuracy of recommendations by introducing social network information and items' tags at the same time.Firstly,users' social network information and items' tags are obtained through crawlers.The Louvain algorithm is used to visually display the users' community distribution,and the user familiarity is calculated based on the breadth-first algorithm.By weighting user familiarity with traditional similarity,a collaborative filtering algorithm that integrates social networks is determined.Then,this paper uses Wilcoxon rank sum test for label selection,then transforms "items-tags " into "users-tags",and combines it with scoring information to obtain the probability distribution of user interest preference.Then,a similarity calculation formula of interest preference based on JS divergence is proposed.By weighting the interest preference similarity with the traditional similarity,a collaborative filtering algorithm for merging item labels is determined.Finally,the cold start problem of the collaborative filtering recommendation algorithm is demonstrated.By defining "cold start" users and "half-cold start" users,as well as combining familiarity and interest preference similarity at the same time,a segmented comprehensive similarity calculation method is proposed to obtain better results.