Session-based Recommendation with Local Invariance

MPhil Thesis Defence


Title: "Session-based Recommendation with Local Invariance"

By

Mr. Tianwen CHEN


Abstract

Session-based recommendation is a task to predict users’ next actions 
given a sequence of previous actions in the same session. Existing methods 
either encode the previous actions in a strict order or completely ignore 
the order. It is not necessary to always capture the sequential 
information in sessions by following a strict order, because sometimes the 
order of actions in a short sub-sequence, called the detailed order, may 
not be important, e.g., when a user is just comparing the same kind of 
products from different brands. We term the property that the order of 
actions in the sub-session level does not matter the local invariance. 
Nevertheless, the high-level ordering information is still useful because 
the data is sequential in nature. Therefore, a good session-based 
recommender should consider the local invariance property while capturing 
the sequential information by paying different attention to the ordering 
information in different levels of granularity. To this end, we propose a 
novel model called LINet to automatically ignore the insignificant 
detailed ordering information in some sub-sessions, while keeping the 
high-level sequential information of the whole sessions. In the model, we 
first use a full self-attention layer with Gaussian weighting to extract 
features of sub-sessions, and then we apply a recurrent neural network to 
capture the high-level sequential information. Extensive experiments on 
two real-world datasets show that our method outperforms or matches the 
state-of-the-art methods and the proposed mechanism to consider the local 
invariance property plays an important role.


Date:			Tuesday, 23 July 2019

Time:			1:00pm - 3:00pm

Venue:			Room 3494
 			Lifts 25/26

Committee Members:	Dr. Raymond Wong (Supervisor)
 			Prof. Gary Chan (Chairperson)
 			Prof. Nevin Zhang


**** ALL are Welcome ****