A Survey of Social Recommendation Systems

PhD Qualifying Examination


Title: "A Survey of Social Recommendation Systems"

by

Mr. Hao GE


Abstract:

In the era of information explosion, recommendation systems (RSs) play an 
increasingly important role in proactively recommending interesting information 
to the users. The recommended information could be about products in online 
shopping malls, videos in video-sharing websites, or news in news websites. RSs 
have been studied extensively in both academia and industry. Traditional RSs 
try to predict a user's preferences on items utilizing only the users' 
historical interactions with the items, which are very commonly represented as 
a user-item rating matrix. Based on the ways predictions are made, approaches 
in traditional RSs can be generally classified into two categories, namely, 
content-based and collaborative filtering-based (CF-based) RSs. In this survey, 
we will review representative works in these two categories.

Alongside RSs, online social media has gained great popularity in the past 
decade as well. People establish social connection with each other to build 
extremely large social networks in different online platforms, e.g., Facebook, 
Twitter, LinkedIn, and so on. To incorporate social information to enhance 
recommendation performance, social RSs have gained great development. Mostly 
social RSs are based on social influence and homophily theory, which states 
that socially connected users share similar preferences and influence each 
other??s taste in activities such as buying products or watching movies. Based 
on how social information is used, in this survey we classify existing social 
RSs research into memory-based and model-based. Due to the fact that Matrix 
Factorization (MF) is the most widely used method in recommendation research. 
Thus, within model-based social RSs, we further review three different ways to 
incorporate social information in MF model. From this survey, we can see that 
traditional RSs can be significantly enhanced by considering social connections 
among users, and that users?? social information is of great value in boosting 
performance of other machine learning tasks as well.


Date:			Monday, 17 June 2019

Time:                  	11:30am - 1:30pm

Venue:                  Room 3494
                         Lifts 25/26

Committee Members:	Prof. Dik-Lun Lee (Supervisor)
 			Dr. Wilfred Ng (Chairperson)
 			Dr. Yangqiu Song
 			Dr. Ke Yi


**** ALL are Welcome ****