Skip to main content
SpringerLink
Log in
Book cover

International Conference on Rough Sets and Knowledge Technology

RSKT 2009: Rough Sets and Knowledge Technology pp 658–665Cite as

A User Profiles Acquiring Approach Using Pseudo-Relevance Feedback

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5589))

Abstract

User profiles are important in personalized Web information gathering and recommendation systems. The current user profiles acquiring techniques however suffer from some problems and thus demand to improve. In this paper, a survey of the existing user profiles acquiring mechanisms is presented first, and a novel approach is introduced that uses pseudo-relevance feedback to acquire user profiles from the Web. The related evaluation result is promising, where the proposed approach is compared with a manual user profiles acquiring technique.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Antoniou, G., van Harmelen, F.: A Semantic Web Primer. MIT Press, Cambridge (2004)

    Google Scholar 

  2. Bollacker, K.D., Lawrence, S., Giles, C.L.: A system for automatic personalized tracking of scientific literature on the Web. In: Proc. of DL 1999, pp. 105–113 (1999)

    Google Scholar 

  3. Cao, G., Nie, J.-Y., Gao, J., Robertson, S.: Selecting good expansion terms for pseudo-relevance feedback. In: Proc. of SIGIR 2008, pp. 243–250 (2008)

    Google Scholar 

  4. Chirita, P.A., Firan, C.S., Nejdl, W.: Personalized query expansion for the Web. In: Proc. of SIGIR 2007, pp. 7–14 (2007)

    Google Scholar 

  5. Collins-Thompson, K., Callan, J.: Estimation and use of uncertainty in pseudo-relevance feedback. In: Proc. of SIGIR 2007, pp. 303–310 (2007)

    Google Scholar 

  6. Gauch, S., Chaffee, J., Pretschner, A.: Ontology-based personalized search and browsing. Web Intelligence and Agent Systems 1(3-4), 219–234 (2003)

    Google Scholar 

  7. Han, J., Chang, K.C.-C.: Data mining for Web intelligence. Computer 35(11), 64–70 (2002)

    Article  Google Scholar 

  8. King, J.D., Li, Y., Tao, X., Nayak, R.: Mining World Knowledge for Analysis of Search Engine Content. Web Intelligence and Agent Systems 5(3), 233–253 (2007)

    Google Scholar 

  9. Kosala, R., Blockeel, H.: Web mining research: A survey. ACM SIGKDD Explorations Newsletter 2(1), 1–15 (2000)

    Article  Google Scholar 

  10. Lee, K.S., Croft, W.B., Allan, J.: A cluster-based resampling method for pseudo-relevance feedback. In: Proc. of SIGIR 2008, pp. 235–242 (2008)

    Google Scholar 

  11. Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: A New Benchmark Collection for Text Categorization Research. Journal of Machine Learning Research 5, 361–397 (2004)

    Google Scholar 

  12. Li, Y., Zhong, N.: Web Mining Model and its Applications for Information Gathering. Knowledge-Based Systems 17, 207–217 (2004)

    Article  Google Scholar 

  13. Li, Y., Zhong, N.: Mining Ontology for Automatically Acquiring Web User Information Needs. IEEE Transactions on Knowledge and Data Engineering 18(4), 554–568 (2006)

    Article  Google Scholar 

  14. Liu, F., Yu, C., Meng, W.: Personalized web search for improving retrieval effectiveness. IEEE Transactions on Knowledge and Data Engineering 16(1), 28–40 (2004)

    Article  Google Scholar 

  15. Lynam, T.R., Buckley, C., Clarke, C.L.A., Cormack, G.V.: A multi-system analysis of document and term selection for blind feedback. In: Proc. of CIKM 2004, pp. 261–269 (2004)

    Google Scholar 

  16. Makris, C., Panagis, Y., Sakkopoulos, E., Tsakalidis, A.: Category ranking for personalized search. Data & Knowledge Engineering 60(1), 109–125 (2007)

    Article  Google Scholar 

  17. Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Transactions on Information Systems (TOIS) 22(1), 54–88 (2004)

    Article  Google Scholar 

  18. Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proc. of HLT 2005, Morristown, NJ, USA, pp. 339–346 (2005)

    Google Scholar 

  19. Robertson, S.E., Soboroff, I.: The TREC 2002 filtering track report. In: TREC (2002)

    Google Scholar 

  20. Sieg, A., Mobasher, B., Burke, R.: Web search personalization with ontological user profiles. In: Proc. of CIKM 2007, pp. 525–534. ACM Press, New York (2007)

    Google Scholar 

  21. Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive web search based on user profile constructed without any effort from users. In: Proc. of WWW 2004, pp. 675–684 (2004)

    Google Scholar 

  22. Tao, T., Zhai, C.: Regularized estimation of mixture models for robust pseudo-relevance feedback. In: Proc. of SIGIR 2006, pp. 162–169 (2006)

    Google Scholar 

  23. Tao, X., Li, Y., Zhong, N., Nayak, R.: Ontology mining for personalzied web information gathering. In: Proc. of WI 2007, pp. 351–358 (2007)

    Google Scholar 

  24. Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing search via automated analysis of interests and activities. In: Proc. of SIGIR 2005, pp. 449–456 (2005)

    Google Scholar 

  25. Trajkova, J., Gauch, S.: Improving ontology-based user profiles. In: Proc. of RIAO 2004, pp. 380–389 (2004)

    Google Scholar 

  26. Yu, S., Cai, D., Wen, J.-R., Ma, W.-Y.: Improving pseudo-relevance feedback in web information retrieval using web page segmentation. In: Proc. of WWW 2003, pp. 11–18 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Science & Technology, Queensland University of Technology, Australia

    Xiaohui Tao & Yuefeng Li

Authors
  1. Xiaohui Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuefeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Engineering and Surveying and Engineering, University of Southern Queensland, QLD 4350, Australia

    Peng Wen

  2. School of Information Technology, Queensland University of Technology, QLD 4001, Brisbane, Australia

    Yuefeng Li

  3. Institute of Mathematics, Warsaw University of Technology, Koszykowa 86, 02008, Warsaw, Poland

    Lech Polkowski

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Yiyu Yao

  5. Faculty of Medicine, Department of Medical Informatics, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shusaku Tsumoto

  6. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tao, X., Li, Y. (2009). A User Profiles Acquiring Approach Using Pseudo-Relevance Feedback. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_83

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02962-2_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02961-5

  • Online ISBN: 978-3-642-02962-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation