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Reseach Article

Content and Location based Information Retrieval System

by J Swathi, G Seethalakshmi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 11
Year of Publication: 2014
Authors: J Swathi, G Seethalakshmi
10.5120/18792-0115

J Swathi, G Seethalakshmi . Content and Location based Information Retrieval System. International Journal of Computer Applications. 107, 11 ( December 2014), 1-4. DOI=10.5120/18792-0115

@article{ 10.5120/18792-0115,
author = { J Swathi, G Seethalakshmi },
title = { Content and Location based Information Retrieval System },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 11 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number11/18792-0115/ },
doi = { 10.5120/18792-0115 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:40:45.873241+05:30
%A J Swathi
%A G Seethalakshmi
%T Content and Location based Information Retrieval System
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 11
%P 1-4
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Personalized Web search is an effective means of providing precise results to different users when they submit the same query. As the amount of web information grows rapidly an efficient personalization approach that modifies the appearance of a website's content to satisfy a specific user's instructions or preferences is required. It is also essential to keep track of the change of interest of the user from time to time. An approach which involves a concept based user profiling strategy, along with the click-through data and keyword-based search, is developed. Concepts are split into content and location concepts and are maintained separately for monitoring the gradual transition in the interest of a user over the time. The user's interest is captured from the click-through information. Depending upon the links clicked and the concepts returned users' information access behavior is analyzed and re-ranking is performed to obtain the relevant results.

References
  1. Margaret H. Dunham, "Data Mining Introductory and Advanced Topic", Delhi, Pearson Education, 2003.
  2. Pallavi Palleti, Harish Karnick, Pabitra Mitra, "Personalized Web Search using Probabilistic Query Expansion", IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshop,2007.
  3. Xuwei Pan, Zhengcheng Wang, Xinjian Gu, "Context- Based Adaptive Personalized Web Search for Improving
  4. Information Retrieval Effectiveness", IEEE, 2007.
  5. Georgia Koutrika, Yannis Ioannidis, "Personalized Queries under a Generalized Preference Model", Proceedings of the 21st International Conference on Data Engineering, 2005.
  6. Fang Liu, Weiyi Meng, "Personalized Web Search for Improving Retrieval Effectiveness", IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 1, January 2004.
  7. E. Agichtein, E. Brill, and S. Dumais, "Improving web search ranking by incorporating user behavior information", in Proc. of ACM SIGIR Conference, 2006.
  8. T. Joachims, "Optimizing search engines using click through data," In Proc. of ACM SIGKDD Conference, 2002.
  9. Q. Gan, J. Attenberg, A. Markowetz, and T. Suel,"Analysis of geographic queries in a search engine log", in Proc. of the International Workshop on Location and the Web, 2008.
  10. S. Yokoji, . Kokono, "A location based search engine", in Proc. of WWW Conference, 2001.
  11. Y. Zhou, X. Xie, C. Wang, Y. Gong, and W. Y. Ma, "Hybrid index structures for location-based Web search", in Proc. of CIKM Conference, 2005.
  12. David Vallet, Pablo Castells, Miriam Fernández, Phivos Mylonas,Yannis Avrithis, "Personalized Content Retrieval in Context Using Ontological Knowledge", IEEE Transactions On Circuits And Systems For Video Technology, Vol. 17, No. 3, March 2007.
  13. K. W. -T. Leung, W. Ng, and D. L. Lee, "Personalized concept-based clustering of search engine queries," IEEE TKDE, vol. 20, no. 11, 2008.
  14. V. K. Priyanka Kolluri, A. Bala Ram, "Efficient Personalized Search using Ranking SVM", International Journal of Computer Science and Information Technologies, Vol. 3 (5) , 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Personalization User-profiling Click-through tf-idf Content and location concept.