Advertisement

An Efficient Collaborative Information Retrieval System by Incorporating the User Profile

  • Hassan Naderi
  • Béatrice Rumpler
  • Jean-Marie Pinon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4398)

Abstract

As the volume of information augments, the importance of the Information Retrieval (IR) increases. Collaborative Information Retrieval (CIR) is one of the popular social-based IR approaches. A CIR system registers the previous user interactions to response to the subsequent user queries more efficiently. But the goals and the characteristics of two users may be different; so when they send the same query to a CIR system, they may be interested in two different lists of documents. In this paper we deal with the personalization problem in the CIR systems by constructing a profile for each user. We propose three new approaches to calculate the user profile similarity that we will employ in our personalized CIR algorithm.

Keywords

Information retrieval personalization personalized collaborative information retrieval 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Armin, H., et al.: Towards Collaborative Information Retrieval: Three Approaches. In: Text Mining - Theoretical Aspects and Applications (2002)Google Scholar
  2. 2.
    Armin, H.: Learning Similarities for Collaborative Information Retrieval. In: Proceedings of the KI-2004 workshop “Machine Learning and Interaction for Text-Based Information Retrieval”, TIR-04, Germany (2004)Google Scholar
  3. 3.
    Fitzpatrick, L., Dent, M.: Automatic Feedback using Past Queries: Social Searching? In: Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 306–313. ACM Press, New York (1997)CrossRefGoogle Scholar
  4. 4.
    Freyne, J., et al.: Further Experiments on Collaborative Ranking in Community-Based Web Search. Artificial Intelligence Review 21(3–4), 229–252 (2004)CrossRefGoogle Scholar
  5. 5.
    Glance, N.S.: Community Search Assistant. In: Proceedings of the International Conference on Intelligent User Interfaces, pp. 91–96. ACM Press, New York (2001)CrossRefGoogle Scholar
  6. 6.
    Gui-Rong, X., et al.: Similarity spreading: a unified framework for similarity calculation of interrelated objects. In: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, New York, USA (2004)Google Scholar
  7. 7.
    I-SPY search engine (25/02/2006), Available on: http://ispy.ucd.ie
  8. 8.
    Raghavan, V.V., Sever, H.: On the Reuse of Past Optimal Queries. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 344–350. ACM Press, New York (1995)CrossRefGoogle Scholar
  9. 9.
    Robertson, S., et al.: Okapi at TREC-3, NIST Special Publication 500-225: the Third Text REtrieval Conference (TREC-3), pp. 109-126Google Scholar
  10. 10.
    Smyth, B., et al.: Collaborative Web Search. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI-03, Acapulco, Mexico, pp. 1417–1419. Morgan Kaufmann, San Francisco (2003)Google Scholar
  11. 11.
    Smyth, B., et al.: A Live User Evaluation of Collaborative Web Search. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, Edinburgh, Scotland (2005)Google Scholar
  12. 12.
    The site of TREC: Text REtrieval Conference (25/02/2006), Available on: http://trec.nist.gov/
  13. 13.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)Google Scholar
  14. 14.
    Voorhees, E.M., Harman, D.K.: Overview of the eighth text retrieval conference (TREC-8). NIST Special Publication 500-246, pp. 1–23 (1999)Google Scholar
  15. 15.
    Wen, J., Nie, J., Zhang, H.: Clustering user queries of a search engine. In: Proc. at 10th International World Wide Web Conference, W3C, pp. 162–168 (2001)Google Scholar
  16. 16.
    Wen, J.: Query clustering using user logs. ACM Transactions on Information Systems 20(1), 59–81 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Hassan Naderi
    • 1
  • Béatrice Rumpler
    • 1
  • Jean-Marie Pinon
    • 1
  1. 1.INSA de LYON, Bâtiment Blaise Pascal, 7, Av. Jean Capelle, F69621 Villeurbanne CedexFrance

Personalised recommendations