Recommendation in heterogeneous information networks with implicit user feedback

Authors:
Xiao Yu;Xiang Ren;Yizhou Sun;Bradley Sturt;Urvashi Khandelwal;Quanquan Gu;Brandon Norick;Jiawei Han
Affiliations:
University of Illinois, at Urbana Champaign, Urbana, IL, USA;University of Illinois, at Urbana Champaign, Urbana, IL, USA;Northeastern University, Boston, MA, USA;University of Illinois, at Urbana Champaign, Urbana, IL, USA;University of Illinois, at Urbana Champaign, Urbana, IL, USA;University of Illinois, at Urbana Champaign, Urbana, IL, USA;University of Illinois, at Urbana Champaign, Urbana, IL, USA;University of Illinois, at Urbana Champaign, Urbana, IL, USA
Venue:
Proceedings of the 7th ACM conference on Recommender systems
Year:
2013

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Abstract

Recent studies suggest that by using additional user or item relationship information when building hybrid recommender systems, the recommendation quality can be largely improved. However, most such studies only consider a single type of relationship, e.g., social network. Notice that in many applications, the recommendation problem exists in an attribute-rich heterogeneous information network environment. In this paper, we study the entity recommendation problem in heterogeneous information networks. We propose to combine various relationship information from the network with user feedback to provide high quality recommendation results. The major challenge of building recommender systems in heterogeneous information networks is to systematically define features to represent the different types of relationships between entities, and learn the importance of each relationship type. In the proposed framework, we first use meta-path-based latent features to represent the connectivity between users and items along different paths in the related information network. We then define a recommendation model with such latent features and use Bayesian ranking optimization techniques to estimate the model. Empirical studies show that our approach outperforms several widely employed implicit feedback entity recommendation techniques.