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MnRec: A News Recommendation Fusion Model Combining Multi-granularity Information

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Neural Information Processing (ICONIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1791))

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Abstract

Personalized news recommendations can alleviate information overload. Most current representation matching-based news recommendation methods learn user interest representations from users’ behavior to match candidate news to perform recommendations. These methods do not consider candidate news during user modeling. The learned user interests are matched with candidate news in the last step, weakening the fine-grained matching signals (word-level relationships) between users and candidate news. Recent research has attempted to address this issue by modeling fine-grained interaction matching between candidate news and each news article viewed by the user. Although interaction-based news recommendation methods can better grasp the semantic focus in the news and focus on word-level behavioral interactions, they may not be able to abstract high-level user interest representations from the news users browse. Therefore, it is a worthwhile problem to make full use of the above two architectures effectively so that the model can discover richly detailed cues of user interests from fine-grained behavioral interactions and the abstraction of high-level user interests representations from the news that users browse. To address this issue, we propose MnRec, a framework for fusing multigranularity information for news recommendation. The model integrates the two matching methods via interactive attention and representation attention. In addition, we design a granularity network module to extract news multigranularity information. We also design an RTCN module to implement multilevel interest modeling of users. Extensive experiments on the real news dataset MIND verify the method’s validity.

This work was supported in part by Shandong Province Key R &D Program (Major Science and Technology Innovation Project) Project under Grants 2020CXGC010102 and the National Key Research and Development Plan under Grant No. 2019YFB1404701.

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Notes

  1. 1.

    https://competitions.codalab.org/competitions/24122.

  2. 2.

    https://msnews.github.io.

  3. 3.

    https://github.com/microsoft/recommenders.

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Authors and Affiliations

  1. Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Laiping Cui, Zhenyu Yang, Yu Wang & Kaiyang Ma

  2. Ocean University of China, Jinan, China

    Guojing Liu

Authors
  1. Laiping Cui
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  2. Zhenyu Yang
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  3. Guojing Liu
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  4. Yu Wang
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  5. Kaiyang Ma
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Corresponding author

Correspondence to Zhenyu Yang .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cui, L., Yang, Z., Liu, G., Wang, Y., Ma, K. (2023). MnRec: A News Recommendation Fusion Model Combining Multi-granularity Information. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_29

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  • DOI: https://doi.org/10.1007/978-981-99-1639-9_29

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  • Print ISBN: 978-981-99-1638-2

  • Online ISBN: 978-981-99-1639-9

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