research-article

Mixed Information Flow for Cross-Domain Sequential Recommendations

Authors:

Maarten de RijkeAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 4

Article No.: 64, Pages 1 - 32

https://doi.org/10.1145/3487331

Published: 08 January 2022 Publication History

Abstract

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this article, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit and a knowledge transfer unit. The proposed mixed information flow network is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users’ current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that the proposed mixed information flow network is able to improve recommendation performance in different domains by modeling mixed information flow. In this article, we focus on the application of mixed information flow networks to a scenario with two domains, but the method can easily be extended to multiple domains.

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Index Terms

Mixed Information Flow for Cross-Domain Sequential Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 4

August 2022

529 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3505210

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 08 January 2022

Accepted: 01 September 2021

Revised: 01 July 2021

Received: 01 December 2020

Published in TKDD Volume 16, Issue 4

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Key Research and Development Program of China
Natural Science Foundation of China
Key Scientific and Technological Innovation Program of Shandong Province
Natural Science Foundation of Shandong Province
Tencent WeChat Rhino-Bird Focused Research Program
Fundamental Research Funds of Shandong University
National Key R&D Program of China

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Cited By

WANG QDI YHUANG LWANG GLIU Y(2024)TECDR: Cross-Domain Recommender System Based on Domain Knowledge Transferor and Latent Preference ExtractorIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7175E107.D:5(704-713)Online publication date: 1-May-2024
https://doi.org/10.1587/transinf.2023EDP7175
Sun KLi CQian T(2024)City Matters! A Dual-Target Cross-City Sequential POI Recommendation ModelACM Transactions on Information Systems10.1145/366428442:6(1-27)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3664284
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