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Reseach Article

Article:A Hybrid Context Based Approach for Web Information Retrieval

by W. Aisha Banu, Dr. P. Sheikh Abdul Kader
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 7
Year of Publication: 2010
Authors: W. Aisha Banu, Dr. P. Sheikh Abdul Kader
10.5120/1493-2010

W. Aisha Banu, Dr. P. Sheikh Abdul Kader . Article:A Hybrid Context Based Approach for Web Information Retrieval. International Journal of Computer Applications. 10, 7 ( November 2010), 25-28. DOI=10.5120/1493-2010

@article{ 10.5120/1493-2010,
author = { W. Aisha Banu, Dr. P. Sheikh Abdul Kader },
title = { Article:A Hybrid Context Based Approach for Web Information Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 10 },
number = { 7 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 25-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume10/number7/1493-2010/ },
doi = { 10.5120/1493-2010 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:59:07.022878+05:30
%A W. Aisha Banu
%A Dr. P. Sheikh Abdul Kader
%T Article:A Hybrid Context Based Approach for Web Information Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 10
%N 7
%P 25-28
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Information retrieval mechanisms from the web are a great need of the hour as the amount of the content is growing dynamically every day. There are many algorithms which have been proposed in literature mainly relying on the output of the search engines. These algorithms are either content based or snippet based and perform a clustered outcome re-ranking of the content for the user. This work proposes a hybrid approach to content clustering that combines the best of the web information retrieval methods and also uses the personal preference information of the users modeling a wide range of contexts. This work introduces a context mechanism of the users in the overall process and presents taxonomy of the methods to organize the output of the search engines. Experimental results are promising and show that this approach has great promise for a wide range of queries.

References
  1. S. Nunes. 2007. Exploring temporal evidence in web information retrieval. BCS IRSG Symposium: Future Directions in Information Access.
  2. R. Manmat`ha, T. Rath and T. Feng. 2001. Modeling score distributions for combining the outputs of search engines. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York.
  3. Yu-Ting Liu, Tie-Yan Liu, Tao Qin, Zhi- Ming Ma, and Hang Li. 2007. Supervised rank aggregation. Analyzing Partially Ranked Data, In Proc. of the International World Wide Web Conference Notes in Statistics. Springer-Verlag, 1985. (WWW),
  4. L Si, J Callan. 2003. A Semi supervised Learning Method to Merge Search Engine Results , ACM Transactions on Information Systems (TOIS), - portal.acm.org.
  5. Alexandre Klementiev, Dan Roth, and Kevin Small. 2008. Unsupervised rank aggregation with distance-based models. In Proc. of the International Conference on Machine Learning (ICML).
  6. Chen, P.-M. and Kuo, F. 2000. C. An information retrieval system based on a user profile. J. Syst. Softw., 54 (1). 3-8.
  7. Mylonas, P. and Avrithis, Y., 2005. Context modeling for multimedia analysis. in Proc. of 5th International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT â€TM05), (Paris, France)
  8. icrosssing, How America searches Mobile, Technical Report. www.icrossing.com/.../How%20America%20Searches%20-%20Mobile.pdf
  9. Zamir, O., and Etzioni, O. 1998. Web document clustering: a feasibility demonstration. In SIGIR ’98: Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval, 46–54.New York, NY, USA: ACM
Index Terms

Computer Science
Information Sciences

Keywords

Web search Context based search Information retrieval