Improve Recommendation Lists Through Neighbor diversification
Recommender systems have been accepted as a vital application on the web by offering product advice or information that users might be interested in. Most research up to this point has focused on improving the accuracy of recommender systems. In this paper we argue that recommendation list diversification is also important in promoting users satisfaction for the users multiple interests, and propose a novel recommendation algorithm which aims to balance the recommendation accuracy and diversity by selecting diverse neighbors in trust based recommender systems. A series of experiments show that the algorithm can improve the recommendation diversity.
recommender syste trust neighbor diversification recommendation list diversity
Fuguo Zhang
School of Information Management,Jiangxi University of Finance & Economics,Nanchang,330013,China
国际会议
上海
英文
2037-2040
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）