Skip to main content
SpringerLink
Log in

Dynamic Facet Hierarchy Constructing for Browsing Web Search Results Efficiently

  • Conference paper
  • First Online:
Current Approaches in Applied Artificial Intelligence (IEA/AIE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9101))

Abstract

In this paper, a method is proposed to dynamically construct a faceted interface to help users navigate web search results for finding required data efficiently. The proposed method consists of two processing steps: 1) candidate facets extraction, and 2) facet hierarchy construction. At first, the category information of entities in Wikipedia and a learning model are used to select the query-dependent facet terms for constructing the facet hierarchy. Then an objective function is designed to estimate the average browsing cost of users when accessing the search results by a given facet hierarchy. Accordingly, two greedy based algorithms, one is a bottom-up approach and another one is a top-down approach, are proposed to construct a facet hierarchy for optimizing the objective function. A systematic performance study is performed to verify the effectiveness and the efficiency of the proposed algorithms.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agarwal, D. B., Chen, C.: fLDA: matrix factorization through latent dirichlet allocation. In: The Third ACM International Conference on WSDM (2010)

    Google Scholar 

  2. Blei, D.M., Ng, A.Y., Jordanm, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research (2003)

    Google Scholar 

  3. Carpineto, C., Osiński, S., Romano, G., Weiss, D.: A survey of web clustering engines. ACM Computing Surveys (CSUR) 41(3), July 2009

    Google Scholar 

  4. Hoffman, M.D., Blei, D.M., Wang, C., Paisley, J.: Stochastic variational inference. The Journal of Machine Learning Research 14(1), January 2013

    Google Scholar 

  5. Jiang, P., Hou, H., Chen, L., Chen, S., Yao, C., Li, C., Wang, M.: Wiki3C: exploiting wikipedia for context-aware concept categorization. In: The Sixth ACM International Conference on Web Search and Data Mining (WSDM) (2013)

    Google Scholar 

  6. Kong, W., Allan, J.: Extracting query facets from search results. In: The 36th International Conference on Research and Development in Information Retrieval (2013)

    Google Scholar 

  7. Li, C., Yan, N., Roy S. B., Lisham, L., Das, G.: Facetedpedia: dynamic generation of query-dependent faceted interfaces for wikipedia. In: The 19th International Conference on World Wide Web (WWW), pp. 651–660 (2010)

    Google Scholar 

  8. Mei, Q., Shen, X.C., Zhai, X.: Automatic labeling of multinomial topic models. In: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2007)

    Google Scholar 

  9. Scaiella, U., Ferragina, P., Marino, A., Ciaramita, M.: Topical clustering of search results. In: The Fifth ACM International Conference on Web Search and Data Mining (2012)

    Google Scholar 

  10. Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423 (1948)

    Article  MATH  MathSciNet  Google Scholar 

  11. Vosecky, J., Jiang, D., Leung, K. W., Ng, W.: Dynamic multi-faceted topic discovery in twitter. In: The 22nd ACM International Conference on Information and Knowledge Management (2013)

    Google Scholar 

  12. Zhu, J., Ahmed, A., Xing, E.P.: MedLDA: maximum margin supervised topic models. The Journal of Machine Learning Research (2012)

    Google Scholar 

  13. Zhu, X., Ming, Z.Y., Zhu, X., Chua, T.S.: Topic hierarchy construction for the organization of multi-source user generated content. In: The 36th ACM International Conference on Research and Development in Information Retrieval (SIGIR) (2013)

    Google Scholar 

  14. http://tagme.di.unipi.it/tagme_help.html

Download references

Author information

Authors and Affiliations

  1. Department of Information Science and Computer Engineering, National Taiwan Normal University, Taipei, Taiwan, Republic of China

    Wei Chang & Jia-Ling Koh

Authors
  1. Wei Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jia-Ling Koh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jia-Ling Koh .

Editor information

Editors and Affiliations

  1. Texas State University, San Marcos, Texas, USA

    Moonis Ali

  2. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Young Sig Kwon

  3. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Chang-Hwan Lee

  4. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Juntae Kim

  5. Seoul National University, Seoul, Korea, Republic of (South Korea)

    Yongdai Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Chang, W., Koh, JL. (2015). Dynamic Facet Hierarchy Constructing for Browsing Web Search Results Efficiently. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19066-2_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19065-5

  • Online ISBN: 978-3-319-19066-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation