research-article

Text Matching Improves Sequential Recommendation by Reducing Popularity Biases

Authors:

Ge YuAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 1534 - 1544

https://doi.org/10.1145/3583780.3615077

Published: 21 October 2023 Publication History

Abstract

This paper proposes Text mAtching based SequenTial rEcommenda-tion model (TASTE), which maps items and users in an embedding space and recommends items by matching their text representations. TASTE verbalizes items and user-item interactions using identifiers and attributes of items. To better characterize user behaviors, TASTE additionally proposes an attention sparsity method, which enables TASTE to model longer user-item interactions by reducing the self-attention computations during encoding. Our experiments show that TASTE outperforms the state-of-the-art methods on widely used sequential recommendation datasets. TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems. Our further analyses illustrate that TASTE significantly improves the recommendation accuracy by reducing the popularity bias of previous item id based recommendation models and returning more appropriate and text-relevant items to satisfy users. All codes are available at https://github.com/OpenMatch/TASTE.

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Cited By

Geng BHuan ZZhang XHe YZhang LYuan FZhou JMo LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User BehaviorsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657974(2311-2315)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657974
Huan ZDing KLi AZhang XMin XHe YZhang LZhou JMo LGu JLiu ZZhong WZhang GLi CYuan FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Exploring Multi-Scenario Multi-Modal CTR Prediction with a Large Scale DatasetProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657865(1232-1241)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657865
Liu ZChen ZZhang MDuan SWen HLi LLi NGu YYu GChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Modeling User Viewing Flow using Large Language Models for Article RecommendationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648305(83-92)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648305

Index Terms

Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
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Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
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University of Birmingham, UK
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Program Chairs:
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Spotify, UK
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Min Zhang
Tsinghua University, China
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Federal University of Minas Gerais, Brazil

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Published: 21 October 2023

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

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Cited By

Geng BHuan ZZhang XHe YZhang LYuan FZhou JMo LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User BehaviorsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657974(2311-2315)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657974
Huan ZDing KLi AZhang XMin XHe YZhang LZhou JMo LGu JLiu ZZhong WZhang GLi CYuan FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Exploring Multi-Scenario Multi-Modal CTR Prediction with a Large Scale DatasetProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657865(1232-1241)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657865
Liu ZChen ZZhang MDuan SWen HLi LLi NGu YYu GChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Modeling User Viewing Flow using Large Language Models for Article RecommendationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648305(83-92)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648305

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