Improving Item Ranking by Leveraging Dual Roles Influence

Ranking items to users is a typical recommendation task, which evaluates users’ preferences for certain items over others. Easy access to social networks has motivated researchers to incorporating trust information for recommendation. In this paper, aiming at offering fundamental support to the trust-based research for item recommendation, we conduct an in-depth analysis on Epinions, Ciao, and FilmTrust data sets. We find that a user’s selection of an item is influenced not only by her trustees but also by her trusters. We leverage this “dual roles influence” to derive two more accurate matrix factorization (MF)-based ranking models, namely, BPRDR and FSDR, respectively. In more detail, the first BPRDR model performs three pairwise preferences comparisons under the Bayesian personal ranking framework, considering the dual roles influence in its ranking assumptions. The second FSDR is an improved factored similarity model as it incorporates dual roles influence to contribute its ranking scores. Extensive experiments on three data sets show that it is essential to consider the dual roles influence when generating top- $K$ item recommendation.