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The past few years has witnessed the great success of recommender systems,which can significantly help users find relevant and interesting items for themin the information era. However, a vast class of researches in this area mainlyfocus on predicting missing links in bipartite user-item networks (representedas behavioral networks). Comparatively, the social impact, especially thenetwork structure based properties, is relatively lack of study. In this paper,we firstly obtain five corresponding network-based features, including useractivity, average neighbors' degree, clustering coefficient, assortativecoefficient and discrimination, from social and behavioral networks,respectively. A hybrid algorithm is proposed to integrate those features fromtwo respective networks. Subsequently, we employ a machine learning process touse those features to provide recommendation results in a binary classifiermethod. Experimental results on a real dataset, Flixster, suggest that theproposed method can significantly enhance the algorithmic accuracy. Inaddition, 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 behavioralnetworks. Furthermore, since the features based on the behavioral networkcontain more diverse and meaningfully structural information, they play a vitalrole in uncovering users' potential preference, which, might show light indeeply understanding the structure and function of the social and behavioralnetworks.

Social Recommender Systems Based on Coupling Network Structure Analysis