IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 8    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 8,August 2012
A novel approach for Query Recommendation Via query logs
Full Text(PDF, )  PP.941-946  
Rachna Chaudhary, Nikita Taneja
Query recommendation, Query Log, Search Engine, Web, and Query Clustering, Query Similarity, Information Retreaival.
Query recommendation is an essential part of modern search engines. Recently, search engines become more critical for finding information over the World Wide Web where web content growing fast, the user's satisfaction of search engine results is decreased. Query Logs are important information repositories, which record user activities on the search results. The mining of these logs can improve the performance of search engines .The technology for enabling query recommendations is query-log mining, which is used to leverage information concerning how people make the use of search engines, and how they rephrase their queries while they looking for information. The proposed system based on learning from query logs predicts user information needs. To carry out the required task, the approach first mines the query logs. Meanwhile, query similarity between the pair wise queries is to be calculated which is based on query contents and their clicked URLs to perform query clustering. Most favored queries are discovered within every query cluster. The proposed result optimization system also presents a query recommendation scheme towards better information retrieval to enhance the search engine efficiency and effectiveness to a large scale.
[1] D.Beeferman and A. Berger. Agglomerative Clustering of a Search Engine Query log. In KDD, pages 407-416, Boston, MA USA, 2000.

[2] J.Wen Nie and H.Zhang, Clustering user queries of a Search Engine. In Proceedings at 10th international World Wide Web Conference, pp 162-168, W3C, 2001.

[3] H. Cui, J-R Wen, J. Y.Nie and Lo. Y. Ma. “Query Expansion by Mining User Logs”. IEEE Trans. On Knowledge and Data Engineering, Press July pp-829-839.2003.

[4] B.M Fonseca, P.B Golger, E.S.De Moura and N.Ziviani, “ Using Association rules to discovery Search Engine related queries ”, proc. of first Latin American Web Congress, Santiago, Chile, Nov. 2003.

[5] Baeza- Yates, R. , Hurt ado, C., and Mendoza, M. “Query recommendation using query logs in search engine”. In proc. of int. Workshop on clustering information over the web, Crete, Springer pp 558-596, 2004

[6] Xuedong Shi and Christopher C. Yang. ” Mining related queries from web search engine query logs” an improved association rule mining model”. Wiley Periodicals, Inc. Published Online 3 August 2007 in Wiley Interscience.

[7] Ql Liu, Mingui Jiang, Zhi Chen.” Query Recommendation with the TF-IQF Model and Popularity Factor”. Proceedings of IEEE factor Beijing, China 2008.

[8] Neelam Duhan, A.K Sharma.”Rank Optimization and Query Recommendation in Search Engine using Web Log Mining Technique. Journal of Computing. Vol 2, Issue 12, Dec. 2010

[9] A.K Sharma, Neelam Duhan, Neha Aggarwal, Ranjana Gupta. “Web Search Result optimization by Mining the Search Engine Query Logs”. Proceedings of International Conference on methods and models in Computer Science, Delhi, India, Dec.13-14, 2010.

[10] Aris Anagnostopoules, Luca Becchetti, Carlos Castillo, Aristides Gionis.” An Optimization Framework for Query Recommendation”. WSDM February 4-6, New York, USA, 2010.

[11] Hamada M.Zahera, GI-Wahed.”Gamal F. El. Hady, Waiel, F.Abd EIWahed.” Query Recommendation for improving Search Engine Results”. Proceedings of the world congress on engineering and Computer Science Vol.1, October 20-22. San Franciso, USA. 2010.

Untitled Page