ROUND: Walking on an object-user heterogeneous network for personalized recommendations

The rapid growth of the world-wide-web has been challenging information sciences for the effective screening of useful information from a vast amount of online resources. Although recent studies have suggested that recommendation approaches relying on the concept of complex networks usually exhibit excellent performance, there still lacks a unified framework to guide the design of a recommender system from the viewpoint of network inference. Besides, two critical questions for a network-based approach, the quality of the object-user network and the measure of the strength of association between an object node and a user node in such a network, are still not systematically explored in existing studies. Aiming to answer these questions, here we introduce a general framework for network-based top-N recommendation and propose a novel method named ROUND that integrates (i) relationships among objects, (ii) relationships among users, and (iii) relationships between objects and users, in a single network model. We adopt a k-nearest neighbor strategy to filter out unreliable connections in the network, and we use a random walk with restart model to characterize the strength of associations between object nodes and user nodes, thereby making significant progress in addressing the critical questions in network-based recommendation. We demonstrate the effectiveness of our method via large-scale cross-validation experiments across two real datasets (MovieLens and Netflix) and show the superiority of our method over such state-of-the-art approaches as non-negative matrix factorization and singular value decomposition in terms of not only recommendation accuracy and diversity but also retrieval performance.