  How can we leverage social network data and observed ratings to correctly
recommend proper items and provide a persuasive explanation for the
recommendations? Many online services provide social networks among users, and
it is crucial to utilize social information since recommendation by a friend is
more likely to grab attention than the one from a random user. Also, explaining
why items are recommended is very important in encouraging the users' actions
such as actual purchases. Exploiting both ratings and social graph for
recommendation, however, is not trivial because of the heterogeneity of the
data.
  In this paper, we propose UniWalk, an explainable and accurate recommender
system that exploits both social network and rating data. UniWalk combines both
data into a unified graph, learns latent features of users and items, and
recommends items to each user through the features. Importantly, it explains
why items are recommended together with the recommendation results. Extensive
experiments show that UniWalk provides the best explainability and achieves the
state-of-the-art-accuracy.
