Use of social network information to enhance collaborative filtering performance

摘要: When people make decisions, they usually rely on recommendations from friends and acquaintances. Although collaborative filtering (CF), the most popular recommendation technique, utilizes similar neighbors to generate recommendations, it does not distinguish in a neighborhood strangers who have tastes. Because social networking Web sites now easy gather network information, study about use of information making will probably produce productive results. In this study, we developed way increase effectiveness by incorporating into CF. We collected data users' preference ratings their relationships site. Then, evaluated CF performance with diverse neighbor groups combining nearest neighbors. Our results indicated that more accurate prediction algorithms can be produced