research-article

Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation

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Chong-Kwon KimAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 293 - 303

https://doi.org/10.1145/3511808.3557434

Published: 17 October 2022 Publication History

Abstract

A cross-domain recommendation has shown promising results in solving data-sparsity and cold-start problems. Despite such progress, existing methods focus on domain-shareable information (overlapped users or same contexts) for a knowledge transfer, and they fail to generalize well without such requirements. To deal with these problems, we suggest utilizing review texts that are general to most e-commerce systems. Our model (named SER) uses three text analysis modules, guided by a single domain discriminator for disentangled representation learning. Here, we suggest a novel optimization strategy that can enhance the quality of domain disentanglement, and also debilitates detrimental information of a source domain. Also, we extend the encoding network from a single to multiple domains, which has proven to be powerful for review-based recommender systems. Extensive experiments and ablation studies demonstrate that our method is efficient, robust, and scalable compared to the state-of-the-art single and cross-domain recommendation methods.

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Index Terms

Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation
1. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

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Published: 17 October 2022

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Liu WZheng XChen CXu JLiao XWang FTan YOng YSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Reducing item discrepancy via differentially private robust embedding alignment for privacy-preserving cross domain recommendationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693387(32455-32470)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693387
Kapkiç AMandal PWan SSheth PGorantla AChoi YLiu HCandan KSerra ESpezzano F(2024)Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679218(5220-5224)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679218
Li ZAmagata DZhang YHara THaruta SYonekawa KKurokawa MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Mutual Information-based Preference Disentangling and Transferring for Non-overlapped Multi-target Cross-domain RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657780(2124-2133)Online publication date: 11-Jul-2024
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Choi YAzad F(2024)Improving the Text Convolution Mechanism with Large Language Model for Review-Based Recommendation2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825014(6977-6981)Online publication date: 15-Dec-2024
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