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A Personalized News Recommendation System Based on Tag Dependency Graph

  • Conference paper
  • First Online:
Web-Age Information Management (WAIM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9098))

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Abstract

The tags of news articles give readers the most important and relevant information regarding the news articles, which are more useful than a simple bag of keywords extracted from news articles. Moreover, latent dependency among tags can be used to assign tags with different weight. Traditional content-based recommendation engines have largely ignored the latent dependency among tags. To solve this problem, we implemented a prototype system called PRST, which is presented in this paper. PRST builds a tag dependency graph to capture the latent dependency among tags. The demonstration shows that PRST makes news recommendation more effectively.

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References

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Author information

Authors and Affiliations

  1. Tianjin University of Technology, Tianjin, 300384, China

    Pengqiang Ai, Yingyuan Xiao, Ke Zhu & Ching-Hsien Hsu

  2. Tianjin Key Lab of Intelligence Computing and Novel Software Technology, Tianjin, 300384, China

    Yingyuan Xiao

  3. Donghua University, Shanghai, 201620, China

    Hongya Wang

  4. Chung Hua University, Hsinchu, 30012, Taiwan

    Ching-Hsien Hsu

Authors
  1. Pengqiang Ai
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  2. Yingyuan Xiao
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  3. Ke Zhu
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  4. Hongya Wang
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  5. Ching-Hsien Hsu
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Corresponding author

Correspondence to Yingyuan Xiao .

Editor information

Editors and Affiliations

  1. Google, CA, USA

    Xin Luna Dong

  2. Postdoc Apartments (Hong Lou) 4-1-4, Shandong University, Li Cheng, Jinan, China

    Xiaohui Yu

  3. Tsinghua University, Beijing, China

    Jian Li

  4. Northeastern University, BOSTON, USA

    Yizhou Sun

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Ai, P., Xiao, Y., Zhu, K., Wang, H., Hsu, CH. (2015). A Personalized News Recommendation System Based on Tag Dependency Graph. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9098. Springer, Cham. https://doi.org/10.1007/978-3-319-21042-1_68

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  • DOI: https://doi.org/10.1007/978-3-319-21042-1_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21041-4

  • Online ISBN: 978-3-319-21042-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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