A Survey on Incremental Update for Neural Recommender Systems

PhD Qualifying Examination


Title: "A Survey on Incremental Update for Neural Recommender Systems"

by

Mr. Peiyan ZHANG


Abstract:

Recommender Systems (RS) aim to provide personalized suggestions of items for 
users against consumer over-choice. Although extensive research has been 
conducted to address different aspects and challenges of RS, there still exists 
a gap between academic research and industrial applications. Specifically, most 
of the existing models still work in an offline manner, in which the 
recommender is trained on a large static training set and evaluated on a very 
restrictive testing set in a one-time process. RS will stay unchanged until 
next batch retrain is performed. We frame such RS as Batch Update Recommender 
Systems (BURS). In reality, they have to face the challenges where RS are 
expected to be instantly updated with new data streaming in, and generate 
updated recommendations for current user activities based on the newly arrived 
data. We frame such RS as Incremental Update Recommender Systems (IURS).

In this article, we offer a systematic survey of incremental update for neural 
r ecommender systems. We begin the survey by introducing key concepts and 
formulating the task of IURS. We then illustrate the challenges in IURS 
compared with traditional BURS. Afterwards, we detail the introduction of 
existing literature and evluation issues. We conclude the survey by outlining 
some prominent open research issues in this area.


Date:			Thursday, 2 December 2021

Time:                  	1:30pm - 3:30pm

Venue:			Room 3494
 			(lifts 25/26)

Committee Members:	Dr. Sunghun Kim (Supervisor)
 			Prof. Nevin Zhang (Chairperson)
 			Prof. Raymond Wong
 			Prof. Xiaofang Zhou


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