Exploring implicit hierarchical structures for recommender systems
Suhang Wang, Jiliang Tang, Yilin Wang, Huan Liu · 2015
Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user pref-erences also present hierarchical structures. Re-cent studies show that incorporating the explicit hi-erarchical structures of items or user preferences can improve the performance of recommender sys-tems. However, explicit hierarchical structures are usually unavailable, especially those of user prefer-ences. Thus, there’s a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of explor-ing the implicit hierarchical structures for recom-mender systems when they are not explicitly avail-able. We propose a novel recommendation frame-work HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effective-ness of the proposed framework. 1