  With the advent of online social networks, recommender systems have became
crucial for the success of many online applications/services due to their
significance role in tailoring these applications to user-specific needs or
preferences. Despite their increasing popularity, in general recommender
systems suffer from the data sparsity and the cold-start problems. To alleviate
these issues, in recent years there has been an upsurge of interest in
exploiting social information such as trust relations among users along with
the rating data to improve the performance of recommender systems. The main
motivation for exploiting trust information in recommendation process stems
from the observation that the ideas we are exposed to and the choices we make
are significantly influenced by our social context. However, in large user
communities, in addition to trust relations, the distrust relations also exist
between users. For instance, in Epinions the concepts of personal "web of
trust" and personal "block list" allow users to categorize their friends based
on the quality of reviews into trusted and distrusted friends, respectively. In
this paper, we propose a matrix factorization based model for recommendation in
social rating networks that properly incorporates both trust and distrust
relationships aiming to improve the quality of recommendations and mitigate the
data sparsity and the cold-start users issues. Through experiments on the
Epinions data set, we show that our new algorithm outperforms its standard
trust-enhanced or distrust-enhanced counterparts with respect to accuracy,
thereby demonstrating the positive effect that incorporation of explicit
distrust information can have on recommender systems.
