Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust

• We propose two Clustering-based Collaborative Filtering (CF) algorithms.
• We design a model-based approach able to combine trust and similarity among users.
• Trust-aware CF increases the coverage of predictions without affecting the quality.
• Item-based Fuzzy C-means CF increases recommendation accuracy (real dataset).

Several approaches for recommending products to the users are proposed in literature, and collaborative filtering has been proved to be one of the most successful techniques. Some issues related to the quality of recommendation and to computational aspects still arise (e.g., cold-start recommendations). In this paper, we investigate the application of model-based Collaborative Filtering (CF) techniques and in particular propose a clustering CF framework and two clustering CF algorithms: Item-based Fuzzy Clustering Collaborative Filtering (IFCCF) and Trust-aware Clustering Collaborative Filtering (TRACCF). We compare several approaches by means of Epinions, MovieLens, Jester, and Poste Italiane datasets (with real customers). Experimental results show an increased value of coverage of the recommendations provided by TRACCF without affecting recommendation quality. Moreover, trust information guarantees high level recommendation for different users.