DYNAMIC QUERY EXPANSION BASED ON USER’S REAL TIME IMPLICIT FEEDBACK

Sanasam Ranbir Singh, Hema A. Murthy, Timothy A. Gonsalves

Abstract

Majority of the queries submitted to search engines are short and under-specified. Query expansion is a commonly used technique to address this issue. However, existing query expansion frameworks have an inherent problem of poor coherence between expansion terms and user’s search goal. User’s search goal, even for the same query, may be different at different instances. This often leads to poor retrieval performance. In many instances, user’s current search is influenced by his/her recent searches. In this paper, we study a framework which explores user’s implicit feedback provided at the time of search to determine user’s search context. We then incorporate the proposed framework with query expansion to identify relevant query expansion terms. From extensive experiments, it is evident that the proposed framework can capture the dynamics of user’s search and adapt query expansion accordingly.

References

  1. Agichtein, E., Brill, E., Dumais, S., and Ragno, R. (2006). Learning user interaction models for predicting web search result preferences. In SIGIR'06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 3-10. ACM.
  2. Attar, R. and Fraenkel, A. S. (1977). Local feedback in fulltext retrieval systems. Journal of ACM, 24(3):397- 417.
  3. Billerbeck, B., Scholer, F., Williams, H. E., and Zobel, J. (2003). Query expansion using associated queries. In CIKM 7803: Proceedings of the twelfth international conference on Information and knowledge management. ACM.
  4. Billerbeck, B. and Zobel, J. (2005). Document expansion versus query expansion for ad-hoc retrieval. In ADCS 7805: Proceedings of the tenth Australasian document computing symposium.
  5. Broder, A. (2002). A taxonomy of web search. SIGIR Forum, 36(2):3-10.
  6. Carroll, J. M. and Rosson, M. B. (1987). Paradox of the active user. In Interfacing thought: cognitive aspects of human-computer interaction, pages 80-111. MIT Press.
  7. Craig, S., Monika, H., Hannes, M., Monika, H., and Michael, M. (1999). Analysis of a very large web search engine query log. SIGIR Forum, 33(1):6-12.
  8. Croft, W. B. and Harper, D. J. (1979). Using probabilistic model of document retrieval without relevance information. Journal of Documentation, 35:285-295.
  9. He, B. and Ounis, I. (2005). Term frequency normalisation tuning for bm25 and dfr model. In ECIR'05: Proceedings of the 27th European Conference on IR Research, pages 200-214.
  10. Jaime, T., Eytan, A., Rosie, J., and Michael, A. S. P. (2007). Information re-retrieval: Repeat queries in yahoo's logs. In SIGIR07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 151-158. ACM.
  11. Jansen, B. J., Spink, A., and Saracevic, T. (2000). Real life, real users, and real needs: a study and analysis of user queries on the web. Information Processing and Management, 36(2):207-227.
  12. Joachims, T. (2002). Optimizing search engines using clickthrough data. In SIGKDD'02: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, pages 133-142. ACM.
  13. Jones, S. (1971). Automatic keyword classification for information retrieval. Butterworths, London, UK.
  14. Kelly, D. and Teevan, J. (2003). Implicit feedback for inferring user preference: A bibliography. SIGIR Forum, 32(2):18-28.
  15. Lee, J. H. (1995). Combining multiple evidence from different properties of weighting schemes. In SIGIR 7895: Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, pages 180-188, New York, NY, USA. ACM.
  16. Qiu, Y. and Frei, H. (1993). Concept based query expansion. In SIGIR93: Proceeding of the 16th International ACM SIGIR Conference on Research and development in information retrieval, pages 151-158. ACM.
  17. Ranbir, S. S., Murthy, H. A., and Gonsalves, T. A. (2008). Effect of word density on measuring words association. In ACM Compute, pages 1-8.
  18. Ranbir, S. S., Murthy, H. A., and Gonsalves, T. A. (2010). Feature selection for text classification based on gini coefficient of inequality. In FSDM'10: Proceedings of the Fourth International Workshop on Feature Selection in Data Mining, pages 76-85.
  19. Salton, G., Wong, A., and Yang, C. S. (1975). A vector space model for automatic indexing. ACM Communication, 18(11):613-620.
  20. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Survey, 34(1):1-47.
  21. Xu, J. and Croft, W. B. (1996). Query expansion using local and global document analysis. In SIGIR'96: Proceedings of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 4-11.
  22. Xu, J. and Croft, W. B. (2000). Improving the effectiveness of information retrieval with local context analysis. ACM Transaction on Information System, 18(1):79- 112.
  23. Yang, Y. and Pedersen, J. O. (1997). A comparative study on feature selection in text categorization. In ICML'97: Proceedings of the Fourteenth International Conference on Machine Learning, pages 412- 420. ACM.
Download


Paper Citation


in Harvard Style

Ranbir Singh S., A. Murthy H. and A. Gonsalves T. (2010). DYNAMIC QUERY EXPANSION BASED ON USER’S REAL TIME IMPLICIT FEEDBACK . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 112-121. DOI: 10.5220/0003104901120121


in Bibtex Style

@conference{kdir10,
author={Sanasam Ranbir Singh and Hema A. Murthy and Timothy A. Gonsalves},
title={DYNAMIC QUERY EXPANSION BASED ON USER’S REAL TIME IMPLICIT FEEDBACK},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={112-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003104901120121},
isbn={978-989-8425-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - DYNAMIC QUERY EXPANSION BASED ON USER’S REAL TIME IMPLICIT FEEDBACK
SN - 978-989-8425-28-7
AU - Ranbir Singh S.
AU - A. Murthy H.
AU - A. Gonsalves T.
PY - 2010
SP - 112
EP - 121
DO - 10.5220/0003104901120121