Research on Personalized Recommendation Algorithm Based on Mobile Social Network Data

Abstract

AbstractDue to the lack of mining of hidden data in traditional personalized recommendation algorithms, the algorithm is interfered by the mobile social network environment, and it is difficult to accurately recommend targeted data for users. Therefore, research on personalized recommendation algorithms based on mobile social network data. By dividing mobile social network user categories, user information is obtained; based on mobile social network data, user demand characteristics are extracted; potential association rules between users and service needs are mined to build personalized recommendation algorithms. The experimental results show that compared with the traditional recommendation algorithm, the research algorithm has stronger perception and recognition ability, and it can recommend more matching information for users according to different user needs when facing different network environments.KeywordsMobile social network dataPersonalizationRecommendation algorithmAssociation rules