The Collaborative Filtering Method Based on Social Information Fusion

In the social network, similar users are assumed to prefer similar items, so searching the similar users of a target user plays an important role for most collaborative filtering methods. Existing collaborative filtering methods use user ratings of items to search for similar users. Nowadays, abundant social information is produced by the Internet, such as user profiles, social relationships, behaviors, interests, and so on. Only using user ratings of items is not sufficient to recommend wanted items and search for similar users. In this paper, we propose a new collaborative filtering method using social information fusion. Our method first uses social information fusion to search for similar users and then updates the user rating of items for recommendation using similar users. Experiments show that our method outperforms the existing methods based on user ratings of items and using social information fusion to search similar users is an available way for collaborative filtering methods of recommender systems.