  Recommender systems play a significant role in providing the appropriate data
for each user among a huge amount of information. One of the important roles of
a recommender system is to predict the preference of each user to some specific
data. Some of these systems concentrate on user-item networks that each user
rates some items. The main step for item recommendation is to predict the rate
of unrated items. Each recommender system utilizes different criteria such as
the similarity between users or social relations in the process of rate
prediction. As social connections of each user affect his behaviors, it can be
a valuable source to use in rate prediction. In this paper, we will provide a
new social recommender system which uses Bhattacharyya coefficient in
similarity computing to be able to evaluate similarity in sparse data and
between users without co-rated items as well as integrating social ties into
the rating prediction process.
