News Recommendation with Attention Mechanism

Authors

  • Tianrui Liu University of California San Diego
  • Changxin Xu Northern Arizona University
  • Yuxin Qiao Universidad Internacional Isabel I de Castilla
  • Chufeng Jiang The University of Texas at Austin
  • Weisheng Chen Xinhua College of Sun Yat-sen University

DOI:

https://doi.org/10.5281/zenodo.10635481

Keywords:

News Recommendation, Natural Language Processing, Machine Learning, Attention

Abstract

This paper explores the area of news recommendation, a key component of online information sharing. Initially, we provide a clear introduction to news recommendation, defining the core problem and summarizing current methods and notable recent algorithms. We then present our work on implementing the NRAM (News Recommendation with Attention Mechanism), an attention-based approach for news recommendation, and assess its effectiveness. Our evaluation shows that NRAM has the potential to significantly improve how news content is personalized for users on digital news platforms.

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Author Biographies

Tianrui Liu, University of California San Diego

Tianrui Liu obtained his Master of Science degree in machine learning and data science from University of California San Diego. His research interests include machine learning, natural language processing, recommendation systems and robotics.

Changxin Xu, Northern Arizona University

Affiliation: Northern Arizona University.

Yuxin Qiao, Universidad Internacional Isabel I de Castilla

Affiliation: Universidad Internacional Isabel I de Castilla, Spain.

Chufeng Jiang, The University of Texas at Austin

Affiliation: Department of Computer Science, The University of Texas at Austin.

Weisheng Chen, Xinhua College of Sun Yat-sen University

Affiliation: Xinhua College of Sun Yat-sen University, China.

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Published

2024-02-12

How to Cite

[1]
T. Liu, C. Xu, Y. Qiao, C. Jiang, and W. Chen, “News Recommendation with Attention Mechanism”, Journal of Industrial Engineering & Applied Science, vol. 2, no. 1, pp. 21–26, Feb. 2024.

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Articles