An intelligent recommender system using social trust path for recommendations in web-based social networks

摘要

In this paper, we combine a social regularization approach that incorporates social network information to benefit recommender systems with the trust information between users. Both trust and rating records (tags) are employed to predict the missing values (tags) in the user-item matrix. Especially, we use an algorithm for best recommended trust path selection, to identify multiple recommended trust paths and to determine an aggregate path for generating different final recommendations. Empirical analyses on real datasets show that the combination of social information and trust achieves superior performance to existing approaches.