Augmenting collaborative recommender by fusing explicit social relationships

Nowadays social websites have become a major trend in the Web 2.0 environment, enabling abundant social data available. In this paper, we explore the role of two types of social relationships: membership and friendship, while being fused with traditional CF (Collaborative Filtering) recommender methods in order to more accurately predict users’ interests and produce recommendations to them. Through an exploratory evaluation with real-life dataset from Last.fm, we have revealed respective effects of the two explicit relationships and furthermore their combinative impacts. In addition, the fusion is conducted via random walk graph model in comparison with via weighted neighborhood similarity matrix, so as to identify the best performance platform. Indepth analysis on the experimental data particularly shows the significant improvement by up to 8% on recommendation accuracy, by embedding social relationships in CF via graph model.