research-article

Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation

Authors:

Jingchao SuAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 1

Article No.: 14, Pages 1 - 31

https://doi.org/10.1145/3522762

Published: 09 January 2023 Publication History

Abstract

Cross domain recommendation (CDR) is one popular research topic in recommender systems. This article focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learning the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of choosing samples to train the mapping. In this article, we attempt to learn both features of user preferences in a more principled way. We assume that each user’s preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformation. Based on this assumption, we propose an equivalent transformation learner (ETL), which models the joint distribution of user behaviors across domains. The equivalent transformation in ETL relaxes the idea of shared-user representation and allows the learned preferences in different domains to preserve the domain-specific features as well as the overlapped features. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-the-art methods. Codes and data are available online: https://github.com/xuChenSJTU/ETL-master.

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Index Terms

Toward Equivalent Transformation of User Preferences in 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 41, Issue 1

January 2023

759 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3570137

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 09 January 2023

Online AM: 09 March 2022

Accepted: 27 February 2022

Revised: 23 November 2021

Received: 14 September 2020

Published in TOIS Volume 41, Issue 1

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National Key R&D Program of China
111 plan
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State Key Laboratory of UHD Video and Audio Production and Presentation
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A*STAR Centre for Frontier AI Research (CFAR)

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