research-article

Learning a Hierarchical Intent Model for Next-Item Recommendation

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Hui XiongAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 2

Article No.: 38, Pages 1 - 28

https://doi.org/10.1145/3473972

Published: 27 September 2021 Publication History

Abstract

A session-based recommender system (SBRS) captures users’ evolving behaviors and recommends the next item by profiling users in terms of items in a session. User intent and user preference are two factors affecting his (her) decisions. Specifically, the former narrows the selection scope to some item types, while the latter helps to compare items of the same type. Most SBRSs assume one arbitrary user intent dominates a session when making a recommendation. However, this oversimplifies the reality that a session may involve multiple types of items conforming to different intents. In current SBRSs, items conforming to different user intents have cross-interference in profiling users for whom only one user intent is considered. Explicitly identifying and differentiating items conforming to various user intents can address this issue and model rich contextual information of a session. To this end, we design a framework modeling user intent and preference explicitly, which empowers the two factors to play their distinctive roles. Accordingly, we propose a key-array memory network (KA-MemNN) with a hierarchical intent tree to model coarse-to-fine user intents. The two-layer weighting unit (TLWU) in KA-MemNN detects user intents and generates intent-specific user profiles. Furthermore, the hierarchical semantic component (HSC) integrates multiple sets of intent-specific user profiles along with different user intent distributions to model a multi-intent user profile. The experimental results on real-world datasets demonstrate the superiority of KA-MemNN over selected state-of-the-art methods.

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Cited By

Zhu NSun LLuo XCao JZhang QLu X(2024)Exploitation or Exploration Next? User Behavior Decoupling and Emerging Intent Modeling for Next-Item Recommendation2024 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM59182.2024.00123(965-970)Online publication date: 9-Dec-2024
https://doi.org/10.1109/ICDM59182.2024.00123
Xu YLi TYang YChen WYue L(2024)An adaptive category-aware recommender based on dual knowledge graphsInformation Processing & Management10.1016/j.ipm.2023.10363661:3(103636)Online publication date: May-2024
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https://dl.acm.org/doi/10.1016/j.eswa.2023.121773
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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 2

April 2022

587 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3484931

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 27 September 2021

Accepted: 01 July 2021

Revised: 01 May 2021

Received: 01 August 2020

Published in TOIS Volume 40, Issue 2

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Cited By

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https://doi.org/10.1109/ICDM59182.2024.00123
Xu YLi TYang YChen WYue L(2024)An adaptive category-aware recommender based on dual knowledge graphsInformation Processing & Management10.1016/j.ipm.2023.10363661:3(103636)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2023.103636
Lian YZhang LSong C(2024)Target-driven user preference transferring recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121773238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121773
Yin YWang JArnob Barua HBao X(2024)TIENExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121403236:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121403
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