  Previous studies show that recommendation algorithms based on historical
behaviors of users can provide satisfactory recommendation performance. Many of
these algorithms pay attention to the interest of users, while ignore the
influence of social relationships on user behaviors. Social relationships not
only carry intrinsic information of similar consumption tastes or behaviors,
but also imply the influence of individual to its neighbors. In this paper, we
assume that social relationships and historical behaviors of users are related
to the same factors. Based on this assumption, we propose an algorithm to focus
on social relationships useful for recommendation systems through mutual
constraints from both types of information. We test the performance of our
algorithm on four types of users, including all users, active users, inactive
users and cold-start users. Results show that the proposed algorithm
outperforms benchmarks in four types of scenarios subject to recommendation
accuracy and diversity metrics. We further design a randomization model to
explore the contribution of social relationships to recommendation performance,
and the result shows that the contribution of social relationships in the
proposed algorithm depends on the coupling strength of social relationships and
historical behaviors.
