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✨ Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations

Authors:

Toyotaro Suzumura,

Irene LiAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 24 - 34

https://doi.org/10.1145/3604915.3608801

Published: 14 September 2023 Publication History

Abstract

Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using advanced natural language processing techniques to extract semantic information from rich textual data, employing content-based methods derived from local historical news. However, this approach lacks a global perspective, failing to account for users’ hidden motivations and behaviors beyond semantic information. To address this challenge, we propose a novel model called GLORY (Global-LOcal news Recommendation sYstem), which combines global representations learned from other users with local representations to enhance personalized recommendation systems. We accomplish this by constructing a Global-aware Historical News Encoder, which includes a global news graph and employs gated graph neural networks to enrich news representations, thereby fusing historical news representations by a historical news aggregator. Similarly, we extend this approach to a Global Candidate News Encoder, utilizing a global entity graph and a candidate news aggregator to enhance candidate news representation. Evaluation results on two public news datasets demonstrate that our method outperforms existing approaches. Furthermore, our model offers more diverse recommendations1.

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Cited By

Chen Y(2024)A News Recommendation Method for User Privacy ProtectionInternational Journal of Computer Science and Information Technology10.62051/ijcsit.v2n3.042:3(25-36)Online publication date: 28-May-2024
https://doi.org/10.62051/ijcsit.v2n3.04
Liu DGreene DLi IJiang XDong R(2024)Topic-Centric Explanations for News RecommendationACM Transactions on Recommender Systems10.1145/36802953:2(1-25)Online publication date: 23-Jul-2024
https://dl.acm.org/doi/10.1145/3680295
Zhang GLi DGu HLu TGu N(2024)Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News RecommendationACM Transactions on the Web10.1145/364988618:3(1-33)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.1145/3649886
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Index Terms

✨ Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 14 September 2023

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RecSys '23

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Chen Y(2024)A News Recommendation Method for User Privacy ProtectionInternational Journal of Computer Science and Information Technology10.62051/ijcsit.v2n3.042:3(25-36)Online publication date: 28-May-2024
https://doi.org/10.62051/ijcsit.v2n3.04
Liu DGreene DLi IJiang XDong R(2024)Topic-Centric Explanations for News RecommendationACM Transactions on Recommender Systems10.1145/36802953:2(1-25)Online publication date: 23-Jul-2024
https://dl.acm.org/doi/10.1145/3680295
Zhang GLi DGu HLu TGu N(2024)Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News RecommendationACM Transactions on the Web10.1145/364988618:3(1-33)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.1145/3649886
Bakhshizadeh MMaus HDengel A(2024)Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life DataProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688068(654-659)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688068
Li IDong RLi LChen L(2024)EARL: Workshop on Evaluating and Applying Recommendation Systems with Large Language ModelsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687110(1262-1264)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687110
Wang SWang WZhang XWang YLiu HChen FBaeza-Yates RBonchi F(2024)A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671944(3200-3211)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671944
Liu DYang BDu HGreene DHurley NLawlor ADong RLi ISerra ESpezzano F(2024)RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679987(3902-3906)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679987
Ding YWang BCui XXu M(2024)Popularity prediction with semantic retrieval for news recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123308247:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123308
Zhou XLin NZheng WZhou DYang A(2024)A contrastive news recommendation framework based on curriculum learningUser Modeling and User-Adapted Interaction10.1007/s11257-024-09422-035:1Online publication date: 28-Dec-2024
https://doi.org/10.1007/s11257-024-09422-0
Zuo ZLu JTan LGong DChen JLiu F(2024)Knowledge-aware user multi-interest modeling method for news recommendationKnowledge and Information Systems10.1007/s10115-024-02290-5Online publication date: 16-Dec-2024
https://doi.org/10.1007/s10115-024-02290-5

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