Selective Transfer Learning for Cross Domain Recommendation

MPhil Thesis Defence

Title: "Selective Transfer Learning for Cross Domain Recommendation"

By

Mr. Zhongqi LU


Abstract

Collaborative filtering (CF) aims to predict users' ratings on items 
according to historical user-item preference data. In many real-world 
applications, preference data are usually sparse, which would make models 
overfit and fail to give accurate predictions. Recently, several research 
works show that by transferring knowledge from some manually selected 
source domains, the data sparseness problem could be mitigated. However 
for most cases, parts of source domain data are not consistent with the 
observations in the target domain, which may misguide the target domain 
model building. We propose a novel criterion based on empirical prediction 
error and its variance to better capture the consistency across domains in 
CF settings. Consequently, we embed this criterion into a boosting 
framework to perform selective knowledge transfer. Comparing to several 
state-of-the-art methods, we show that our proposed selective transfer 
learning framework can significantly improve the accuracy of rating 
prediction on several real-world recommendation tasks.


Date:			Tuesday, 18 June 2013

Time:			9:30am - 11:30am

Venue:			Room 4483
 			Lifts 25/26

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Prof. Dit-Yan Yeung (Chairperson)
 			Dr. Raymond Wong


**** ALL are Welcome ****