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News Recommendation via Jointly Modeling Event Matching and Style Matching

  • Conference paper
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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14172))

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Abstract

News recommendation is a valuable technology that helps users effectively and efficiently find news articles that interest them. However, most of existing approaches for news recommendation often model users’ preferences by simply mixing all different information from news content together without in-depth analysis on news content. Such a practice often leads to significant information loss and thus impedes the recommendation performance. In practice, two factors which may significantly determine users’ preferences towards news are news event and news style since users tend to read news articles that report events they are interested in, and they also prefer articles that are written in their preferred style. Such two factors are often overlooked by existing approaches. To address this issue, we propose a novel Event and Style Matching (ESM) model for improving the performance of news recommendation. The ESM model first uses an event-style disentangler to extract event and style information from news articles respectively. Then, a novel event matching module and a novel style matching module are designed to match the candidate news with users’ preference from the event perspective and style perspective respectively. Finally, a unified score is calculated by aggregating the event matching score and style matching score for next news recommendation. Extensive experiments on real-world datasets demonstrate the superiority of ESM model and the rationality of our design (The source code and the splitted datasets are publicly available at https://github.com/ZQpengyu/ESM).

P. Zhao and S. Wang—Equal contribution.

This paper was partially supported by Nature Science Foundation of Shandong under Grant No. ZR2022MF243, National Nature Science Foundation of China under Grant No. 61502259, Key Program of Science and Technology of Shandong under Grant No. 2020CXGC010901, Program of Science and Technology of Qilu University of Technology under Grant No. 2021JC02010.

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Notes

  1. 1.

    https://msnews.github.io/.

  2. 2.

    https://codalab.lisn.upsaclay.fr/competitions/420.

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Authors and Affiliations

  1. School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Pengyu Zhao, Wenpeng Lu, Weiyu Zhang & Chaoqun Zheng

  2. Data Science Institute, University of Technology Sydney, Sydney, Australia

    Shoujin Wang

  3. Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia

    Xueping Peng

  4. School of Computer, Beijing Institute of Technology, Beijing, China

    Yonggang Huang

Authors
  1. Pengyu Zhao
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  2. Shoujin Wang
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  3. Wenpeng Lu
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  4. Xueping Peng
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  5. Weiyu Zhang
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  6. Chaoqun Zheng
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  7. Yonggang Huang
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Corresponding author

Correspondence to Wenpeng Lu .

Editor information

Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

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Zhao, P. et al. (2023). News Recommendation via Jointly Modeling Event Matching and Style Matching. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14172. Springer, Cham. https://doi.org/10.1007/978-3-031-43421-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-43421-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43420-4

  • Online ISBN: 978-3-031-43421-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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