A Novel Framework for Improving Recommender Diversity

Jinpeng Chen, Yu Liu, Jun Hu, Wei He, Deyi Li

Abstract: Recommender systems are being used to assist users in finding relevant items from a large set of alternatives in many online applications. However, while most research up to this point has focused on improving the accuracy of recommender systems, other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we present a novel recommendation framework, designed to balance and diversify personalized top-N recommendation lists in order to capture the user’s complete spectrum of interests. Systematic experiments on the real-world rating data set have demonstrated the effectiveness of our proposed framework in learning both accuracy and diversity of recommendations.