  The past few years has witnessed the great success of recommender systems,
which can significantly help users find relevant and interesting items for them
in the information era. However, a vast class of researches in this area mainly
focus on predicting missing links in bipartite user-item networks (represented
as behavioral networks). Comparatively, the social impact, especially the
network structure based properties, is relatively lack of study. In this paper,
we firstly obtain five corresponding network-based features, including user
activity, average neighbors' degree, clustering coefficient, assortative
coefficient and discrimination, from social and behavioral networks,
respectively. A hybrid algorithm is proposed to integrate those features from
two respective networks. Subsequently, we employ a machine learning process to
use those features to provide recommendation results in a binary classifier
method. Experimental results on a real dataset, Flixster, suggest that the
proposed method can significantly enhance the algorithmic accuracy. In
addition, as network-based properties consider not only the social activities,
but also take into account user preferences in the behavioral networks,
therefore, it performs much better than that from either social or behavioral
networks. Furthermore, since the features based on the behavioral network
contain more diverse and meaningfully structural information, they play a vital
role in uncovering users' potential preference, which, might show light in
deeply understanding the structure and function of the social and behavioral
networks.
