Improving 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 user´s satisfaction for the user´s 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.