research-article

Triple Sequence Learning for Cross-domain Recommendation

Authors: Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, and Jie ZhouAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 4

Article No.: 91, Pages 1 - 29

https://doi.org/10.1145/3638351

Published: 09 February 2024 Publication History

Abstract

Cross-domain recommendation (CDR) aims at leveraging the correlation of users’ behaviors in both the source and target domains to improve the user preference modeling in the target domain. Conventional CDR methods typically explore the dual-relations between the source and target domains’ behaviors. However, this may ignore the informative mixed behaviors that naturally reflect the user’s global preference. To address this issue, we present a novel framework, termed triple sequence learning for cross-domain recommendation (Tri-CDR), which jointly models the source, target, and mixed behavior sequences to highlight the global and target preference and precisely model the triple correlation in CDR. Specifically, Tri-CDR independently models the hidden representations for the triple behavior sequences and proposes a triple cross-domain attention (TCA) method to emphasize the informative knowledge related to both user’s global and target-domain preference. To comprehensively explore the cross-domain correlations, we design a triple contrastive learning (TCL) strategy that simultaneously considers the coarse-grained similarities and fine-grained distinctions among the triple sequences, ensuring the alignment while preserving information diversity in multi-domain. We conduct extensive experiments and analyses on six cross-domain settings. The significant improvements of Tri-CDR with different sequential encoders verify its effectiveness and universality. The source code is available at https://github.com/hulkima/Tri-CDR.

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Cited By

Al-Hasan TSayed ABensaali FHimeur YVarlamis IDimitrakopoulos G(2024)From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future DirectionsBig Data and Cognitive Computing10.3390/bdcc80400368:4(36)Online publication date: 27-Mar-2024
https://doi.org/10.3390/bdcc8040036
Sun WLi MLi PCao XMeng XMeng L(2024)Sequential selection and calibration of video frames for 3D outdoor scene reconstructionCAAI Transactions on Intelligence Technology10.1049/cit2.12338Online publication date: 25-Apr-2024
https://doi.org/10.1049/cit2.12338
Wang YLi XLiu YCao XMeng XMeng L(2024)Causal inference for out‐of‐distribution recognition via sample balancingCAAI Transactions on Intelligence Technology10.1049/cit2.12311Online publication date: 2-Apr-2024
https://doi.org/10.1049/cit2.12311

Index Terms

Triple Sequence Learning for Cross-domain Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 4

July 2024

751 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3613639

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 09 February 2024

Online AM: 22 December 2023

Accepted: 03 December 2023

Revised: 23 October 2023

Received: 21 June 2023

Published in TOIS Volume 42, Issue 4

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National Natural Science Foundation of China
TaiShan Scholars Program
Excellent Youth Scholars Program of Shandong Province
Oversea Innovation Team Project of the ”20 Regulations for New Universities” funding program of Jinan

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Cited By

Al-Hasan TSayed ABensaali FHimeur YVarlamis IDimitrakopoulos G(2024)From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future DirectionsBig Data and Cognitive Computing10.3390/bdcc80400368:4(36)Online publication date: 27-Mar-2024
https://doi.org/10.3390/bdcc8040036
Sun WLi MLi PCao XMeng XMeng L(2024)Sequential selection and calibration of video frames for 3D outdoor scene reconstructionCAAI Transactions on Intelligence Technology10.1049/cit2.12338Online publication date: 25-Apr-2024
https://doi.org/10.1049/cit2.12338
Wang YLi XLiu YCao XMeng XMeng L(2024)Causal inference for out‐of‐distribution recognition via sample balancingCAAI Transactions on Intelligence Technology10.1049/cit2.12311Online publication date: 2-Apr-2024
https://doi.org/10.1049/cit2.12311

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