Skip to main content
SpringerLink
Log in

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))

Included in the following conference series:

  • 2732 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proc. of IUI, Hong Kong, China (2010)

    Google Scholar 

  2. Kompan, M., Bieliková, M.: Content-based news recommendation. In: Buccafurri, F., Semeraro, G. (eds.) EC-Web 2010. LNBIP, vol. 61, pp. 61–72. Springer, Heidelberg (2010)

    Google Scholar 

  3. Li, L., Wang, D., Li, T., et al.: Scene: a scalable two-stage personalized news recommendation system. In: Proc. of SIGIR, New York, USA (2011)

    Google Scholar 

  4. Wei, X., Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: Proc. of SIGIR, New York, USA (2006)

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingyuan Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ke Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongya Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ching-Hsien Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21041-4

  • Online ISBN: 978-3-319-21042-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation