research-article

HDNR: A Hyperbolic-Based Debiased Approach for Personalized News Recommendation

Authors:

Hongbo XuAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 259 - 268

https://doi.org/10.1145/3539618.3591693

Published: 18 July 2023 Publication History

Abstract

Personalized news recommendation aims to recommend candidate news to the target user, according to the clicked news history. The user-news interaction data exhibits power-law distribution, however, existing works usually learn representations in Euclidean space which makes inconsistent capacities between data space and embedding space, leading to severe representation distortion problem. Besides, the existence of conformity bias, a potential cause of power-law distribution, may introduce biased guidance to learn user representations. In this paper, we propose a novel debiased method based on hyperbolic space, named HDNR, to tackle the above problems. Specifically, first, we employ hyperboloid model with exponential growth capacity to conduct user and news modeling, in order to solve inconsistent space capacities problem and obtain low distortion representations. Second, we design a re-weighting aggregation module to further mitigate conformity bias in data distribution, through considering local importance of the clicked news among contextual history and its global popularity degree simultaneously. Finally, we calculate the relevance score between target user and candidate news representations. We conduct experiments on two real-world news recommendation datasets MIND-Large, MIND-Small and empirical results demonstrate the effectiveness of our approach from multiple perspectives.

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Choi YChoi JKo TKim CNejdl WAuer SKarras OCha MMoens MNajork M(2025)Review-Based Hyperbolic Cross-Domain RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703486(146-155)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703486
Zhao LLiu QYue LChen WChen LSun RSong CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)COMI: COrrect and MItigate Shortcut Learning Behavior in Deep Neural NetworksProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657729(218-228)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657729
Guo HWang DSun ZZhang HLi JZhang JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Configurable Fairness for New Item Recommendation Considering Entry Time of ItemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657694(437-447)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657694
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Index Terms

HDNR: A Hyperbolic-Based Debiased Approach for Personalized News Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Publication History

Published: 18 July 2023

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Strategic Priority Research Program of Chinese Academy of Sciences
National Natural Science Foundation of China
National Key Research and Development Program of China
Youth Innovation Promotion Association of CAS

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SIGIR '23

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SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Choi YChoi JKo TKim CNejdl WAuer SKarras OCha MMoens MNajork M(2025)Review-Based Hyperbolic Cross-Domain RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703486(146-155)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703486
Zhao LLiu QYue LChen WChen LSun RSong CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)COMI: COrrect and MItigate Shortcut Learning Behavior in Deep Neural NetworksProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657729(218-228)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657729
Guo HWang DSun ZZhang HLi JZhang JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Configurable Fairness for New Item Recommendation Considering Entry Time of ItemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657694(437-447)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657694
Zhang HTian HChen CTian CDou W(2024)A Blockchain-Assisted Federated Learning Method for Recommendation Systems2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00016(58-65)Online publication date: 30-Oct-2024
https://doi.org/10.1109/ISPA63168.2024.00016
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
https://doi.org/10.1109/BigData62323.2024.10825014
Chen XFan WLi Q(2024)Causal Behavior Pattern Inference for News Recommendation Through Multi-interest MatchingWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_13(179-190)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0570-5_13

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