Issue No. 06 - June (2018 vol. 30)
Suhang Wang , Department of Computer Science and Engineering, Arizona State University, Tempe, AZ
Jiliang Tang , Department of Computer Science and Engineering, Michigan State University, East Lansing, MI
Yilin Wang , Department of Computer Science and Engineering, Arizona State University, Tempe, AZ
Huan Liu , Department of Computer Science and Engineering, Arizona State University, Tempe, AZ
Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the hierarchy of items or user preferences can improve the performance of recommender systems. However, hierarchical structures are often not explicitly available, especially those of user preferences. Thus, there's a gap between the importance of hierarchies and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework to bridge the gap, which enables us to explore the implicit hierarchies of users and items simultaneously. We then extend the framework to integrate explicit hierarchies when they are available, which gives a unified framework for both explicit and implicit hierarchical structures. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework by incorporating implicit and explicit structures.
Music, Recommender systems, Art, Films, Motion pictures, Clocks, Ice
S. Wang, J. Tang, Y. Wang and H. Liu, "Exploring Hierarchical Structures for Recommender Systems," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 6, pp. 1022-1035, 2018.