Trust-Enhanced Recommender System based on Case-based Reasoning and Collaborative Filtering

The emerging environment of recommender systems (RSs) facilitates tailored suggestions to users by mapping users' information space to their particular information requirements. In this paper, we propose a new recommendation scheme that combines case-based reasoning (CBR) with collaborative filtering (CF) and incorporates fuzzy trust model. The CBR methodology is employed to find the most appropriate cluster that forms neighborhood (nbd) set for the active user. The nbd generation process of CBR, based on user rating vector (URV) and clustering, improves system's scalability to certain extent. Additionally, the proposed scheme allows users to decide which other user's opinions they should trust more. In this way, the trustworthy users from the set of neighbors suggested by CBR are filtered by applying a fuzzy trust model. As a consequence, only trustworthy neighbors contribute to the final prediction. Experimental results clearly demonstrate that the proposed recommendation scheme (Trust/CBR/CF) outperforms Pearson CF (PCF) and CBR/CF.