Effective Approaches of Capturing Intra- and Inter-session Relationships for Session-based Recommendation

PhD Thesis Proposal Defence


Title: "Effective Approaches of Capturing Intra- and Inter-session 
Relationships for Session-based Recommendation"

by

Mr. Tianwen CHEN


Abstract:

With the explosive growth of information, recommender systems become a 
critical tool to alleviate the information overload problem in many online 
services such as e-commerce and media sharing websites. Conventional 
recommendation methods such as collaborative filtering rely on tracking 
user identities to model each individual user's preferences, which may 
result in poor performance in scenarios where user identities cannot be 
tracked due to some reasons including anonymous users or privacy issues. 
Session-based recommendation (SBR) tackles this problem by assuming that 
users perform actions on a session basis, where a session is a sequence of 
actions in close temporal proximity. Under this assumption, users' actions 
in the same session are highly correlated, and thus, the sequential and 
co-occurrence patterns in the active session can be utilized to more 
accurately model the current user's preferences. Since SBR does not 
require user information and the "session-based" assumption is a common 
phenomenon, it is of great practical value and has received much attention 
in both academia and industry recently.

The key to building a successful session-based recommender system is to 
effectively utilize the properties of sessions by capturing both intra- 
and inter-session relationships. In this thesis proposal, we introduce 
four studies for accurate session-based recommendation.

The first two studies focus on capturing intra-session relationships. In 
our first study, we consider the local invariance property in SBR, which 
states that the detailed order of user actions in local regions of 
sessions is not meaningful while the high-level order in the entire 
sessions reflect users' intentions. We propose a model that can pay 
different attention to the ordering information in different levels of 
granularity by ignoring the insignificant detailed ordering information in 
some sub-sessions while keeping the high-level sequential information of 
the whole sessions. In our second study, we aim to improve the 
discrimination ability of graph neural network-based methods by addressing 
two information loss problems, namely the lossy session encoding and 
ineffective long-range dependency capturing problems. The first problem, 
lossy session encoding, says that different sessions are encoded to the 
same representation. The second problem, ineffective long-range dependency 
capturing, states that long-range dependencies among items cannot be 
explicitly captured due to the limited number of GNN layers. We propose a 
GNN model that does not have the two information loss problems by 
combining two novel GNN layers.

The other two studies focus on capturing inter-session relationships. In 
our third study, we consider the inter-session relationships in two 
levels, namely the item level and the session level. To capture the 
item-level inter-session relationships, we propose a GNN to automatically 
learn the importance of item co-occurrence patterns from a global graph 
that encodes the fine-grained information about item co-occurrences such 
as relative order and distance. To capture the session-level inter-session 
relationships, we propose four data augmentation techniques and adopt the 
constrastive learning framework to correctly cluster sessions with similar 
semantics. Lastly, we introduce our ongoing work that proposes a framework 
to help existing methods to more efficiently and effectively capture 
inter-session relationships when a social network among users is 
accessible. The proposed framework is able to integrate a variety of 
information such as user attributes and item categories when they are 
available. Existing methods can be plugged into the framework and achieve 
much better recommendation accuracy with the same inference efficiency.

We conduct extensive experiments on the commonly used public benchmark 
datasets and the results show that our methods are more effective than the 
state-of-the-art methods in terms of capturing intra- and inter-session 
relationships.


Date:			Thursday, 16 June 2022

Time:                  	2:00pm - 4:00pm

Zoom Meeting: 
https://hkust.zoom.us/j/97034122118?pwd=Q1luckh0YktkNzJibklqai9RKzNDZz09

Committee Members:	Prof. Raymond Wong (Supervisor)
  			Prof. Gary Chan (Chairperson)
 			Dr. Minhao Cheng
 			Prof. James Kwok


**** ALL are Welcome ****