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With the advent of online social networks, recommender systems have becamecrucial for the success of many online applications/services due to theirsignificance role in tailoring these applications to user-specific needs orpreferences. Despite their increasing popularity, in general recommendersystems suffer from the data sparsity and the cold-start problems. To alleviatethese issues, in recent years there has been an upsurge of interest inexploiting social information such as trust relations among users along withthe rating data to improve the performance of recommender systems. The mainmotivation for exploiting trust information in recommendation process stemsfrom the observation that the ideas we are exposed to and the choices we makeare significantly influenced by our social context. However, in large usercommunities, in addition to trust relations, the distrust relations also existbetween users. For instance, in Epinions the concepts of personal "web oftrust" and personal "block list" allow users to categorize their friends basedon the quality of reviews into trusted and distrusted friends, respectively. Inthis paper, we propose a matrix factorization based model for recommendation insocial rating networks that properly incorporates both trust and distrustrelationships aiming to improve the quality of recommendations and mitigate thedata sparsity and the cold-start users issues. Through experiments on theEpinions data set, we show that our new algorithm outperforms its standardtrust-enhanced or distrust-enhanced counterparts with respect to accuracy,thereby demonstrating the positive effect that incorporation of explicitdistrust information can have on recommender systems.

Matrix Factorization with Explicit Trust and Distrust Relationships