Integrating Opinion Leader and User Preference for Recommendation

Collaborative filtering (CF) is one of the most well-known and commonly used technology for recommender systems. However, it suffers from inherent issues such as data sparsity. Many works have been done by used additional information such as user attributes, tags and social relationships to address these problems. We proposed an algorithm named OLrs (Opinion Leaders for Recommender System) based on the trust relationships. Specifically, the opinion leaders who have a strong influence for the active user and an accurate evaluation of the recommend item will be identified. The prediction for a given item is generated by ratings of these opinion leaders and the active user. Experimental results based on Epinions data set demonstrated that the prediction accuracy of our method outperforms other approach.