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International Conference on Web Information Systems Engineering

WISE 2020: Web Information Systems Engineering – WISE 2019 pp 599–614Cite as

Interaction Graph Neural Network for News Recommendation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11881))

Abstract

Personalized news recommendation has become a highly challenging problem in recent years. Traditional ID-based methods such as collaborative filtering are not suitable for news recommendation due to the extremely rapid update of candidate news. Various content-based methods have been proposed for news recommendation and achieved the state-of-the-art performance. Recently, knowledge-aware news recommendation further improves the performance through discover latent knowledge level connections among the news. However, we argue that the above content-based methods do not fully utilize the collaborative information latent in user-item interactions into user and news representation learning process. In this paper, we propose a new news recommendation model, Interaction Graph Neural Network (IGNN), which integrates a user-item interactions graph and a knowledge graph into the news recommendation model. Specifically, IGNN obtains the representation of users and items with two graphs. One is the knowledge graph, and another is the user-item interaction graph. It learns the content-based feature from knowledge-level and semantic-level with convolutional neural networks and fuses the high-order collaborative signals extracted from the user-item interaction graph into user and news representation learning process with a graph neural network. Extensive experiments are conducted on the two real-world news data sets, and experimental results show that IGNN significantly outperforms the state-of-the-art approaches for news recommendation.

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Acknowledgements

This research was partially supported by NSFC (No. 61876117, 61876217, 61872258, 61728205), Open Program of Key Lab of IIP of CAS (No. IIP2019-1) and PAPD.

Author information

Authors and Affiliations

  1. Institute of AI, School of Computer Science and Technology, Soochow University, Suzhou, China

    Yongye Qian, Pengpeng Zhao, Zhixu Li, Junhua Fang & Lei Zhao

  2. Key Lab of IIP of CAS, Institute of Computing Technology, Beijing, China

    Pengpeng Zhao

  3. The University of Central Arkansas, Conway, USA

    Victor S. Sheng

  4. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China

    Zhiming Cui

Authors
  1. Yongye Qian
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  2. Pengpeng Zhao
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  3. Zhixu Li
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  4. Junhua Fang
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  5. Lei Zhao
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  6. Victor S. Sheng
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  7. Zhiming Cui
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Corresponding author

Correspondence to Pengpeng Zhao .

Editor information

Editors and Affiliations

  1. University of Hong Kong, Hong Kong SAR, China

    Reynold Cheng

  2. University of Ioannina, Ioannina, Greece

    Nikos Mamoulis

  3. University of California, Los Angeles, CA, USA

    Yizhou Sun

  4. Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China

    Xin Huang

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Qian, Y. et al. (2019). Interaction Graph Neural Network for News Recommendation. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds) Web Information Systems Engineering – WISE 2019. WISE 2020. Lecture Notes in Computer Science(), vol 11881. Springer, Cham. https://doi.org/10.1007/978-3-030-34223-4_38

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  • DOI: https://doi.org/10.1007/978-3-030-34223-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34222-7

  • Online ISBN: 978-3-030-34223-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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