Temporal Dynamics in Recommender Systems

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


PhD Thesis Defence


Title: "Temporal Dynamics in Recommender Systems"

By

Mr. Zhongqi LU


Abstract

We investigate on the temporal dynamics phenomenon in recommender systems. 
By analyzing the public dataset from real world applications, we find the 
temporal dynamics phenomenon is common in the online recommender systems, 
and the phenomenon would cause problems in making good recommendations. In 
this thesis, we propose four approaches to tackle the problems caused by 
the temporal dynamics phenomenon. The four approaches are the user's 
autoregressive interests evolution, user's markovian interests evolution, 
a POMDP recommendation framework, and the transfer learning approach. Both 
the user's autoregressive interests evolution and the user's markovian 
interests evolution are motivated by the sequential property in the 
changes of the user's interests. The POMDP recommendation framework is 
inspired by the self-learning mechanism of reinforcement learning models. 
The transfer learning approach is driven by the rich source domain data. 
Overall, the four approaches focus on handling the problems raised by 
temporal dynamics phenomenon in recommender systems. We also discuss the 
metrics and the datasets to verify our proposed approaches.


Date:			Monday, 9 January 2017

Time:			5:00pm - 7:00pm

Venue:			Room 4472
 			Lifts 25/26

Chairman:		Prof. I-Ming Hsing (CBME)

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Prof. James Kwok
 			Prof. Yangqiu Song
 			Prof. Rong Zheng (ISOM)
 			Prof. Irwin King (Comp. Sci. & Engg., CUHK)


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