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Reseach Article

A novel method to Automatically Categorizing Search Results using Web Search Goals

by Rohini B. Mothe, V. S. Deshmukh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 22
Year of Publication: 2014
Authors: Rohini B. Mothe, V. S. Deshmukh
10.5120/17318-3899

Rohini B. Mothe, V. S. Deshmukh . A novel method to Automatically Categorizing Search Results using Web Search Goals. International Journal of Computer Applications. 98, 22 ( July 2014), 35-39. DOI=10.5120/17318-3899

@article{ 10.5120/17318-3899,
author = { Rohini B. Mothe, V. S. Deshmukh },
title = { A novel method to Automatically Categorizing Search Results using Web Search Goals },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 22 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number22/17318-3899/ },
doi = { 10.5120/17318-3899 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:27:01.146907+05:30
%A Rohini B. Mothe
%A V. S. Deshmukh
%T A novel method to Automatically Categorizing Search Results using Web Search Goals
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 22
%P 35-39
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In present days world wide web provides a platform for users to satisfy their information needs, for this purpose search engine tools are commonly used. Available search engine give result for a particular query in the form of flat rank list, which works well for non-ambiguous query. But,in case of ambiguous query which having multiple aspects the flat rank list not works well. So in such cases reorganization of search result is necessary. In this paper, proposed a method which reorganizes search result by analyzing user's implicit feedback. Based upon this feedback doing text processing, enriching each url by combination of title and snippet ,and mapping these data to Pseudo-document. Pseudo-document contain set of keywords which are different aspects of query. And then performing clustering on these pseudo-document using fuzzy k-mean clustering. And these clusters contain links which are most relevant to each other. Also rearranging results based upon most visited links such that it should occur at topmost. And this reorganization will increase the performance and evaluation of search engine. And the cluster labels.

References
  1. Zheng Lu, Hongyuan Zha, Xiaokang Yang, Weiyao Lin, ZhaohuiZheng, "A New Algorithm for Inferring User Search Goals withFeedback Sessions", IEEE Transactions on Knowledge and Data Engineering, Vol. 25, No. 3, pp. 502-513,2013.
  2. R. Jones and K. L. Klinkner, "Beyond the Session Timeout:Automatic Hierarchical Segmentation of Search Topics in QueryLogs," Proc. 17th ACM Conf. Information and Knowledge Management (CIKM '08), pp. 699-708, 2008.
  3. X. Wang and C. -X Zhai, "Learn from Web Search Logs to OrganizeSearch Results," Proc. 30th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '07), pp. 87-94,2007.
  4. D. Shen, J. Sun, Q. Yang, and Z. Chen, "Building Bridges for Web Query Classification," Proc. 29th Ann. Int'l ACM SIGIR Conf Research and Development in Information Retrieval (SIGIR '06),pp. 131-138, 2006.
  5. T. Joachims, L. Granka, B. Pang, H. Hembrooke, and G. Gay,"Accurately Interpreting Click through Data as Implicit Feedback,"Proc. 28th Ann. Int'l ACM SIGIR Conf. Research and Developmentin Information Retrieval (SIGIR '05), pp. 154-161, 2005.
  6. R. Baeza-Yates, C. Hurtado, and M. Mendoza, "Query Recommendation Using Query Logs in Search Engines," Proc. Int'lConf. Current Trends in Database Technology (EDBT '04), pp. 588-596, 2004.
  7. H. -J Zeng, Q. -C He, Z. Chen, W. -Y Ma, and J. Ma, "Learning to Cluster Web Search Results," Proc. 27th Ann. Int'l ACM SIGIRConf. Research and Development in Information Retrieval(SIGIR '04), pp. 210-217, 2004.
  8. H. Chen and S. Dumais, "Bringing Order to the Web: Automatically Categorizing Search Results," Proc. SIGCHI Conf. Human Factors in Computing Systems (SIGCHI'00), pp. 145-152, 2000.
  9. T. Joachims, "Optimizing Search Engines Using ClickthroughData," Proc. Eighth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (SIGKDD '02), pp. 133-142, 2002.
  10. T. Joachims, "Evaluating Retrieval Performance Using ClickthroughData", Text Mining, J. Franke, G. Nakhaeizadeh, and I Renz, eds. , pp. 79-96, Physica/Springer Verlag, 2003.
  11. R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. ACM Press, 1999.
  12. D. Beeferman and A. Berger, "Agglomerative Clustering of a Search Engine Query Log," Proc. Sixth ACM SIGKDD Int'l Conf Knowledge Discovery and Data Mining (SIGKDD '00), pp. 407-416, 2000.
  13. J. -R Wen, J. -Y Nie, and H. -J Zhang, "Clustering User Queries of Search Engine," Proc. Tenth Int'l Conf. World Wide Web(WWW '01), pp. 162-168, 2001.
  14. U. Lee, Z. Liu, and J. Cho, "Automatic Identification of User Goalsin Web Search," Proc. 14th Int'l Conf. World Wide Web(WWW '05), pp. 391-400, 2005.
  15. C. -K Huang, L. -F Chien, and Y. -J Oyang, "Relevant Term Suggestion in Interactive Web Search Based on Contextual Information in Query Session Logs," J. Am. Soc. for Information Science and Technology, vol. 54, no. 7, pp. 638-649, 2003.
  16. O. Zamir and O. Etzioni. "Grouper: A dynamic clustering interface to web search results. Computer Networks", 31(11-16), pp. 1361-1374, 1999.
  17. Xinye Li "An improved method in clustering Web retrieval result based on relevance feedback", Computer Science and Service System (CSSS), IEEE International Conference ,pp. 3000 - 3003,2011.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy k-means clustering Implicit feedback Pseudo-documents User search goals.