Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks.

Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, Dik Lun Lee

Abstract:

Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender
systems, which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level
semantics of recommendations and how to fuse the heterogeneous information to make recommendations. In this paper, we solve the
two problems by €rst introducing the concept of meta-graph to HINbased recommendation, and then solving the information fusion
problem with a “matrix factorization (MF) + factorization machine (FM)” approach. For the similarities generated by each meta-graph,
we perform standard MF to generate latent features for both users and items. With different meta-graph based features, we propose
to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta-graph based
features. Experimental results on two real-world datasets, Amazon and Yelp, show the effectiveness of our approach compared to state-of-the-art FM and other HIN-based recommendation algorithms.