Learning with Heterogeneous Side Information Fusion for Recommender Systems

Collaborative filtering (CF) based methods have become the most popular technique for recommender systems (RSs). In recent years, various types of side information such as social connections among users and metadata of items have been introduced into CF and shown to be effective for improving recommendation performance. Moreover, side information can alleviate data sparsity and cold start problems facing conventional CF based methods. However, previous works process different types of information separately, thus losing information that might exist across different types of side information. In this work, we study the application of Heterogeneous Information Network (HIN), which offers flexible representation of different types of side information, to enhance CF based recommendation methods. Since HIN could be a complex graph representing multiple types of relations between entity types, we need to tackle two challenging issues facing HIN-based RSs: How to capture the complex semantics that determines the similarities between users and items in a HIN, and how to fuse the heterogeneous side information to support recommendation. To address these issues, we apply metagraph to HIN-based RSs and solve the information fusion problem with a "matrix factorization (MF) + factorization machine (FM)" framework. For the MF part, we obtain the user-item similarity matrix from each metagraph and then apply low-rank matrix approximation to obtain latent features for both users and items. For the FM part, we apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and at the same time identify the usefulness of the metagraphs. Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than FM and other state-of-the-art HIN-based recommendation methods.