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News Recommendation Based on Click-Through Rate Prediction Model

In: Liss 2020

Author

Listed:
  • Guiying Wei

    (University of Science and Technology Beijing)

  • Yimeng Wei

    (University of Science and Technology Beijing)

  • Jincheng Lei

    (University of Science and Technology Beijing)

Abstract

News recommendation is one of the most popular applications in recommendation system, but the traditional recommendation algorithms are challenged by news features such as high update frequency, time-sensitive, high proportion of inactive users, large scale of news data, etc. In this paper we propose a news recommendation system based on click-through rate prediction model which has been used in online advertising, and data features are processed by one-hot encoding and gradient boosting decision tree. Comparative experiments proved its effectiveness and superiority in news recommendation.

Suggested Citation

  • Guiying Wei & Yimeng Wei & Jincheng Lei, 2021. "News Recommendation Based on Click-Through Rate Prediction Model," Springer Books, in: Shifeng Liu & Gábor Bohács & Xianliang Shi & Xiaopu Shang & Anqiang Huang (ed.), Liss 2020, pages 373-387, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-4359-7_27
    DOI: 10.1007/978-981-33-4359-7_27
    as

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