Paper Info
Title
Dynamic intent-aware iterative denoising network for session-based recommendation
Abstract
Session-based recommendation aims to predict items that a user will interact with based on historical behaviors in anonymous sessions. It has long faced two challenges: (1) the dynamic change of user intents which makes user preferences towards items change over time; (2) the uncertainty of user behaviors which adds noise to hinder precise preference learning. They jointly preclude recommender system from capturing real intents of users. Existing methods have not properly solved these problems since they either ignore many useful factors like the temporal information when building item embeddings, or do not explicitly filter out noisy clicks in sessions. To tackle above issues, we propose a novel Dynamic Intent-aware Iterative Denoising Network (DIDN) for session-based recommendation. Specifically, to model the dynamic intents of users, we present a dynamic intent-aware module that incorporates item-aware, user-aware and temporal-aware information to learn dynamic item embeddings. A novel iterative denoising module is then devised to explicitly filter out noisy clicks within a session. In addition, we mine collaborative information to further enrich the session semantics. Extensive experimental results on three real-world datasets demonstrate the effectiveness of the proposed DIDN. Specifically, DIDN obtains improvements over the best baselines by 1.66%, 1.75%, and 7.76% in terms of P@20 and 1.70%, 2.20%, and 10.48% in terms of MRR@20 on all datasets.
Year
DOI
Venue
2022
10.1016/j.ipm.2022.102936
Information Processing & Management
Keywords
DocType
Volume
Session-based recommendation,Dynamic intents,Uncertain behavior,Attention mechanism
Journal
59
Issue
ISSN
Citations 
3
0306-4573
0
PageRank 
References 
Authors
0.34
32
7
Name
Order
Citations
PageRank
Xiaokun Zhang100.68
Hongfei Lin200.68
Bo Xu39528.26
Chenliang Li401.35
Yuan Lin510416.38
Haifeng Liu661.22
Fenglong Ma737433.08