A new confidence-based recommendation approach: Combining trust and certainty

Abstract Collaborative Filtering (CF) is one of the most successful recommendation techniques. Recently, implicit trust-based recommendation approaches have emerged that incorporate implicit trust information into CF in order to improve recommendation performance. Previous implicit trust models assume that all users have the same perception of ratings. However, although all users employ members of the same rating domain (e.g. ratings on a 1–5 scale), each individual has his own interpretations about a rating domain in order to express his preferences. Thus, it is reasonable that a user's rating vector has some degree of uncertainty, depending upon the rating usage trend of that user. In this paper, we present a new approach for confidence modeling in the context of recommender systems. The idea of this modeling is that confidence in a particular user depends not only on the trust in the opinions of that user but also on the certainty of these opinions. Based on this idea, we propose a new Confidence-Based Recommendation (CBR) approach. This approach employs four different confidence models that derive the users’ and items’ confidence values from both local and global perspectives. Experimental results on real-world data sets demonstrate the effectiveness of the proposed approach.