Collaborative Filtering Recommendation Algorithm Combining Trust and Popular Punishment
With the development of Internet information, the amount of network data increases greatly, and so does the difficulty of effec-tive information screening for users. A recommendation system generates recommendations based on the historical behaviors and prefer-ences of users. Collaboration filtering is a common algorithm in the recommendation system. When the traditional collaborative filtering al-gorithm only uses similarity as the recommendation basis, it still faces the problem of low recommendation accuracy. In this paper, the trust between users is added on the basis of similarity, the trust relationship between users is modeled, and the punishment mechanism for popular items is added, so as to weaken the recommendation of popular projects. Verified by experimental results on the MovieLens dataset, the TrustUserCF-Based collaborative filtering algorithm has improved the performance of precision, coverage and F1-score com-pared with the traditional user-based collaborative filtering algorithm.
万方数据
维普
