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Reseach Article

An Extension to New Algorithm for Feedback Session

by Pooja Agarwal, Swarupa Sonawane, Pratiksha Tanpure
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 16
Year of Publication: 2015
Authors: Pooja Agarwal, Swarupa Sonawane, Pratiksha Tanpure
10.5120/20423-2723

Pooja Agarwal, Swarupa Sonawane, Pratiksha Tanpure . An Extension to New Algorithm for Feedback Session. International Journal of Computer Applications. 116, 16 ( April 2015), 32-34. DOI=10.5120/20423-2723

@article{ 10.5120/20423-2723,
author = { Pooja Agarwal, Swarupa Sonawane, Pratiksha Tanpure },
title = { An Extension to New Algorithm for Feedback Session },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 16 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 32-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number16/20423-2723/ },
doi = { 10.5120/20423-2723 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:57:19.733590+05:30
%A Pooja Agarwal
%A Swarupa Sonawane
%A Pratiksha Tanpure
%T An Extension to New Algorithm for Feedback Session
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 16
%P 32-34
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Internet is widely used by users to satisfy various information needs. This paper proposed a novel approach to infer user goals. Pseudo documents are generated through feedback sessions. We introduce new criterion"classified average precision(CAP)", for evaluate performance of inferring user search goals. Results are represented on search engine to validate the effectiveness of our work. Extraction of interesting information from web data has become more popular and result of that web mining attracted lot of attention in recent times.

References
  1. Hamada M. Zahera, Gamal F. El Hady, Waiel. F Abd El-Wahed , Query Recommendation Using Query Logs in Search Engines, Proc. Intl Conf. Current Trends in Database Technology (EDBT 04), pp. 588- 596, 2004.
  2. H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li, Context- Aware Query Suggestion by Mining Click-Through, Proc. 14th ACM SIGKDD Intl Conf. Knowledge Discovery and Data Mining (SIGKDD08), pp. 875-883, 2008.
  3. H. Chen and S. Dumais, Bringing Order to the Web: Auto- matically Categorizing Search Results, Proc. SIGCHI Conf. Human Factors in Computing Systems (SIGCHI 00), pp. 145-152, 2000
  4. T. Joachims, Evaluating Retrieval Performance Using Click- through Data, Text Mining, J. Franke, G. Nakhaeizadeh, and I. Renz, eds. , pp. 79-96, Physica/ Springer Verlag, 2003. T. Joachims, Evaluating Retrieval Performance Using Click- through Data, Text Mining,
  5. T. Joachims, Optimizing Search Engines Using Clickthrough Data, Proc. Eighth ACM SIGKDD Intl Conf. Knowledge Discovery and Data Mining (SIGKDD02), pp. 133-142, 2002.
  6. T. Joachims, L. Granka, B. Pang, H. Hembrooke, andG. Gay, Accurately Interpreting Clickthrough Data asImplicit Feed- back, Proc. 28th Ann. Intl ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 05), pp. 154-161, 2005.
  7. R. Jones, B. Rey, O. Madani, and W. Greiner, Generating Query Substitutions, Proc. 15th Intl Conf. World Wide Web (WWW 06), pp. 387-396, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

User search goals feedback session pseudo documents restructuring search result classified average precision.