A novel collective matrix factorization model for recommendation with fine-grained social trust prediction

Recommender systems are playing an increasing role in improving user satisfaction as they can recommend items which might be highly interested to users. Recent advances have proven that social relations such as trust and distrust relations among users are helpful in improving recommendation accuracy. Traditional social recommendation methods directly utilize unweighted trust and distrust relations into collaborative filtering framework. These methods will lose their power when the trust or distrust relation data is sparse, which significantly hinders the improvement of rating prediction accuracy. To address this problem, we transform the unweighted trust and distrust relations into fine-grained weighted social trust matrix which is denser and encodes the trust and distrust degree for pair of users. The weighted social trust matrix is then combined with the rating matrix in a collective matrix factorization framework to implement rating prediction task. Experimental results based on Extended Epinions dataset show that the proposed collective matrix factorization model with fine-grained weighted social trust matrix can achieve better accuracy than conventional social recommendation algorithms such as SoRec and its extensions.