Enhancing Recommender Systems with Rich Side Information

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Enhancing Recommender Systems with Rich Side Information"

By

Mr. Huan ZHAO


Abstract

Collaborative filtering (CF) based methods have become the most popular 
techniques 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 helps to alleviate data 
sparsity and cold start problems of conventional CF based RSs. However, 
previous works process different types of information separately, thus losing 
information that might exist across different types of side information.

In this thesis, we explore methods to enhance RS with various side information. 
We start with the incorporation of an important type of side information, i.e., 
social connections among users, into a state-of-the-art matrix factorization 
(MF) method, namely, Local LOw Rank Matrix Approximation (LLORMA). We propose 
our Social LOcal Matrix Approximation (SLOMA), which exploits social 
relationship in decomposing the user-item matrix into low-rank matrices. 
Experimental results obtained from two real-world datasets demonstrate the 
superiority of SLOMA to LLORMA in the rating prediction task.

Next, we study the application of Heterogeneous Information Network (HIN) to 
enhance CF based recommendation methods. HIN is a flexible scheme for 
representing the connections between different types of information. Since HIN 
could be a complex graph representing multiple types of relations existing 
between entity types, we need to tackle two challenging issues facing HIN-based 
RSs: How to capture the complex semantics determining the similarities between 
users and items in a HIN, and how to fuse the heterogeneous information to 
support recommendation. We propose to apply metagraph to HIN-based RSs to 
overcome the former problem and the ``matrix factorization (MF) + factorization 
machine (FM)'' framework for the latter. 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 propose to apply FM with Group lasso (FMG) on the features obtained 
from the MF part to train the recommendation model and at the same time 
identify the useful metagraphs. Experimental results from 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.

Finally, besides metagraph, we further propose the Motif Enhanced MetaPath 
(MEMP) method for computing the similarities between users and items in 
HIN-based RSs. Motif is a local structure that can capture higher-order 
relations among nodes in homogeneous graphs. We argue that such higher-order 
relations also exist among nodes of same types in HIN. Thus, existing metapath 
based similarities can also be enhanced by integrating these motif-based 
higher-order relations. After computing the MEMP based similarities between 
users and items, we apply our proposed ``MF+FM'' framework to fuse the 
similarities and for rating prediction. Experiments have been conducted on two 
real-word datasets, Epinions and CiaoDVD, the results demonstrate the 
effectiveness of MEMP in HIN-based RSs.


Date:			Monday, 21 January 2019

Time:			3:00pm - 5:00pm

Venue:			Room 3494
 			Lifts 25/26

Chairman:		Prof. Qian Liu (IEDA)

Committee Members:	Prof. Dik-Lun Lee (Supervisor)
 			Prof. Yangqiu Song
 			Prof. Nevin Zhang
 			Prof. Wenbo Wang (MARK)
 			Prof. Kam-Fai Wong (CUHK)


**** ALL are Welcome ****