Transfer Learning in Collaborative Filtering

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


PhD Thesis Defence


Title: "Transfer Learning in Collaborative Filtering"

By

Mr. Weike Pan


Abstract

Transfer learning and collaborative filtering have been studied in each 
community separately since early 1990s and were married in late 2000s. 
Transfer learning is proposed to extract and transfer knowledge from 
auxiliary data to improve the target learning task and has achieved great 
success in text mining, mobile computing, bio-informatics, etc. 
Collaborative filtering is a major intelligent component in various 
recommender systems, like movie recommendation in Netflix, news 
recommendation in Google News, people recommendation in Tencent Weibo 
(microblog), advertisement recommendation in Facebook, etc. Transfer 
learning in collaborative filtering (TLCF) is studied to address the data 
sparsity problem in the user-item preference data in recommender systems.

In this thesis, we develop this new multidisciplinary area mainly from two 
aspects. First, we propose a general learning framework, study four new 
and specific problem settings for movie recommendation and people 
recommendation, and design four novel TLCF solutions correspondingly. 
Second, we survey and categorize traditional transfer learning works into 
model-based transfer, instance-based transfer and feature-based transfer, 
and build a relationship between traditional transfer learning algorithms 
and TLCF solutions from a unified view of model-based transfer, 
instance-based transfer, and feature-based transfer.


Date:			Wednesday, 30 May 2012

Time:			10:00am – 12:00noon

Venue:			Room 3501
 			Lifts 25/26

Chairman:		Prof. Ning Wang (PHYS)

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Prof. Lei Chen
 			Prof. Wilfred Ng
 			Prof. Weichuan Yu (ECE)
                         Prof. Haifeng Wang (Habin Inst. of Tech.)


**** ALL are Welcome ****