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DCAN: Deep Co-Attention Network by Modeling User Preference and News Lifecycle for News Recommendation

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Database Systems for Advanced Applications (DASFAA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12683))

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Abstract

Personalized news recommendation systems aim to alleviate information overload and provide users with personalized reading suggestions. In general, each news has its own lifecycle that is depicted by a bell-shaped curve of clicks, which is highly likely to influence users’ choices. However, existing methods typically depend on capturing user preference to make recommendations while ignoring the importance of news lifecycle. To fill this gap, we propose a Deep Co-Attention Network DCAN by modeling user preference and news lifecycle for news recommendation. The core of DCAN is a Co-Attention Net that fuses the user preference attention and news lifecycle attention together to model the dual influence of users’ clicked news. In addition, in order to learn the comprehensive news representation, a Multi-Path CNN is proposed to extract multiple patterns from the news title, content and entities. Moreover, to better capture user preference and model news lifecycle, we present a User Preference LSTM and a News Lifecycle LSTM to extract sequential correlations from news representations and additional features. Extensive experimental results on two real-world news datasets demonstrate the significant superiority of our method and validate the effectiveness of our Co-Attention Net by means of visualization.

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Notes

  1. 1.

    https://www.kaggle.com/gspmoreira/news-portal-user-interactions-by-globocom.

  2. 2.

    http://reclab.idi.ntnu.no/dataset/.

  3. 3.

    The Adressa dataset contains news title, content and entity information and the Globo dataset only contains news content information.

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Acknowledgment

This work is supported by the National Key R&D Program of China (2019YFB1406302, 2018YFB1003903), National Natural Science Foundation of China (No. 61502033, 61472034, 61772071, 61272361 and 61672098) and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Lingkang Meng, Chongyang Shi & Xiangrui Su

  2. College of Data Science, Taiyuan University of Technology, Shanxi, China

    Shufeng Hao

Authors
  1. Lingkang Meng
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  2. Chongyang Shi
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  3. Shufeng Hao
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  4. Xiangrui Su
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Corresponding author

Correspondence to Chongyang Shi .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  4. The Pennsylvania State University, University Park, PA, USA

    Wang-Chien Lee

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  6. Athens University of Economics and Business, Athens, Greece

    Vana Kalogeraki

  7. National Cheng Kung University, Tainan City, Taiwan

    Jen-Wei Huang

  8. National Tsing Hua University, Hsinchu, Taiwan

    Chih-Ya Shen

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Meng, L., Shi, C., Hao, S., Su, X. (2021). DCAN: Deep Co-Attention Network by Modeling User Preference and News Lifecycle for News Recommendation. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-73200-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73199-1

  • Online ISBN: 978-3-030-73200-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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