  Collaborative filtering (CF) has been one of the most important and popular
recommendation methods, which aims at predicting users' preferences (ratings)
based on their past behaviors. Recently, various types of side information
beyond the explicit ratings users give to items, such as social connections
among users and metadata of items, have been introduced into CF and shown to be
useful for improving recommendation performance. However, previous works
process different types of information separately, thus failing to capture the
correlations that might exist across them. To address this problem, in this
work, we study the application of heterogeneous information network (HIN) to
enhance CF-based recommendation methods. However, we face challenging issues in
HIN-based recommendation, i.e., how to capture similarities of complex
semantics between users and items in a HIN, and how to effectively fuse these
similarities to improve final recommendation performance. To address these
issues, we apply metagraph to similarity computation 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 useful metagraphs.
Besides FMG, a two-stage method, we further propose an end-to-end method,
hierarchical attention fusing (HAF), to fuse metagraph based similarities for
the final recommendation. Experimental results on four large real-world
datasets show that the two proposed frameworks significantly outperform
existing state-of-the-art methods in terms of recommendation performance.

Information Retrieval