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News Recommendation Method Based on Topic Extraction and User Interest Transfer

In: Liss 2021

Author

Listed:
  • Yimeng Wei

    (University of Science and Technology Beijing)

  • Guiying Wei

    (University of Science and Technology Beijing)

  • Sen Wu

    (University of Science and Technology Beijing)

Abstract

Contemporary news reader faces the problem of information explosion which has led to the difficulty for users to acquire information they really need. News recommendation system plays a significant role in screening interested news for users, but traditional news recommendation methods often face cold-start problem and are difficult to reflect the user personalized differences. This paper proposes a news recommendation method based on topic extraction and user interest transfer (NRTU). The proposed model extracts news topic tags by analyzing semantic information from texts which alleviates cold-start problem of news and applies Long-Short Term Memory (LSTM) model to represent user interest transfer. Extensive experiments on a real-world dataset validate the effectiveness of our approach.

Suggested Citation

  • Yimeng Wei & Guiying Wei & Sen Wu, 2022. "News Recommendation Method Based on Topic Extraction and User Interest Transfer," Lecture Notes in Operations Research, in: Xianliang Shi & Gábor Bohács & Yixuan Ma & Daqing Gong & Xiaopu Shang (ed.), Liss 2021, pages 208-219, Springer.
    • Handle: RePEc:spr:lnopch:978-981-16-8656-6_20
    DOI: 10.1007/978-981-16-8656-6_20
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