Google Scholar

##plugins.themes.academic_pro.article.sidebar##

Published
Sep 14, 2014
Download
PDF
Statistic
Read Counter : 24 Download : 21

Downloads

Download data is not yet available.

##plugins.themes.academic_pro.article.main##

Abstract

Today internet users are using web search engines for complex generic query processing to achieve the day to day activities like trip management, budget planning, shopping plan and text similarity etc. To avoid the complex generic query management many search engines are using query modulators, to break the main query in smaller sub queries to reduce the complexity and to extract the relevant data as results. Some search engines also maintain the user level search history customization to help the user by suggesting them. To achieve more efficiency in personalized search, in this paper we are introducing dynamic clustering in personalized search to assist the user search and to improve the precision of search relevance. This clustering is also useful to find result ranking, relevance ratio and result collaboration. Experimental results are showing that our approach is having the high precision and recall in terms of search relevance and scalable in terms of response time than other approaches.

Keywords: web mining, personalized search, dynamic query clustering, search history analysis, query result processing.

##plugins.themes.academic_pro.article.details##

Author Biographies

Bethapudi Haritha, Vasireddy Venkatadri Institute of Technology Nambur (v), Guntur, Andhra Pradesh.

M.Tech

K Mohana Krishna, Vasireddy Venkatadri Institute of Technology Nambur (v), Guntur, Andhra Pradesh.

Asst Professor
How to Cite
Haritha, B., & Krishna, K. M. (2014). Dynamic Query Clustering in Personalized Search to Improve Retrieval Relevance. International Journal of Emerging Trends in Science and Technology, 1(07). Retrieved from http://igmpublication.org/ijetst.in/index.php/ijetst/article/view/337

References

1. J. Yi and F. Maghoul, “Query clustering using click-through graph,” in WWW, 2009.
2. E. Sadikov, J. Madhavan, L. Wang, and A. Halevy, “Clustering query refinements by user intent,” in WWW, 2010.
3. A. Z. Broder, S. C. Glassman, M. S. Manasse and G. Zweig, Syntactic clustering of the Web, in: Proceedings of the Sixth International Web Wide World Conference (WWW6), 1997.
4. D. R. Cutting, D. R. Karger and J. O. Pedersen, Constant interaction-time Scatter/Gather browsing of large document collections, in: (SIGIR’93), 1993, pp 126-135.
5. A. Spink, M. Park, B. J. Jansen, and J. Pedersen, “Multitasking during Web search sessions,” Information Processing and Management, vol. 42, no. 1, pp. 264–275, 2006.
6. O. Zamir, Visualization of search results in document retrieval systems, General Examination Report, University of Washington, 1998.
7. M. A. Hearst, The use of categories and clusters in information access interfaces, in: T. Strzalkowski (Ed.), Natural Language Information Retrieval, Kluwer Academic Publishers, 1998.
8. R. Baeza-Yates and A. Tiberi, “Extracting semantic relations from query logs,” in KDD, 2007.
9. A. Spoerri, InfoCrystal: A visual tool for information retrieval and management, in: Proceedings of Information Knowledge and Management (CIKM'03), 2003, pp 150-157.
10. T. Radecki, “Output ranking methodology for documentclustering-based boolean retrieval systems,” in SIGIR. New York, NY, USA: ACM, 1985, pp. 70–76.
11. F. Radlinski and T. Joachims, “Query chains: Learning to rank from implicit feedback,” in KDD, 2005.
12. L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank citation ranking: Bringing order to the web,” in Technical report, Stanford University, 1998.
13. P. Boldi, M. Santini, and S. Vigna, “Pagerank as a function of the damping factor,” in WWW, 2005.