  Recently, there is a surge of social recommendation, which leverages social
relations among users to improve recommendation performance. However, in many
applications, social relations are absent or very sparse. Meanwhile, the
attribute information of users or items may be rich. It is a big challenge to
exploit these attribute information for the improvement of recommendation
performance. In this paper, we organize objects and relations in recommendation
system as a heterogeneous information network, and introduce meta path based
similarity measure to evaluate the similarity of users or items. Furthermore, a
matrix factorization based dual regularization framework SimMF is proposed to
flexibly integrate different types of information through adopting the
similarity of users and items as regularization on latent factors of users and
items. Extensive experiments not only validate the effectiveness of SimMF but
also reveal some interesting findings. We find that attribute information of
users and items can significantly improve recommendation accuracy, and their
contribution seems more important than that of social relations. The
experiments also reveal that different regularization models have obviously
different impact on users and items.
