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Reseach Article

Query Recommendation for Long Tail Queries-A Review Paper

by Anand Prasad Gupta, Sunita Yadav
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 13
Year of Publication: 2012
Authors: Anand Prasad Gupta, Sunita Yadav
10.5120/4880-7315

Anand Prasad Gupta, Sunita Yadav . Query Recommendation for Long Tail Queries-A Review Paper. International Journal of Computer Applications. 39, 13 ( February 2012), 14-17. DOI=10.5120/4880-7315

@article{ 10.5120/4880-7315,
author = { Anand Prasad Gupta, Sunita Yadav },
title = { Query Recommendation for Long Tail Queries-A Review Paper },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 13 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number13/4880-7315/ },
doi = { 10.5120/4880-7315 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:22.080657+05:30
%A Anand Prasad Gupta
%A Sunita Yadav
%T Query Recommendation for Long Tail Queries-A Review Paper
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 13
%P 14-17
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Large volume of queries over large volume of users allows search engine to build methods for generating query suggestion for input query. Query recommendation methods are powerful technique to generate related queries or alternate queries as a query suggestion for original query which is given by user in the search engine first time. In this review paper we discussed about query recommendation methods and comparison between different methods how the query suggestion is given by these methods for the input query by user in search engine. Some query recommendation method do not covers the unseen and rare queries but some of them covers these queries by some additional feature added such as generalize the query token of input query by a suitable place holder from hierarchy tool wordnet3.0 or wekepedia by yago mapping. These generalization techniques are also discussed in this paper.

References
  1. Idan Szpektor, Aristides Gionis, Yoelle Maarek (2009). “Improving Recommendation for Long-tail Queries via Templates”, International World Wide WebConference, WWW 2009.
  2. Q. Mei, D. Zhou, and K. Church (In CIKM ’08). “Query suggestion using hitting time“, proceeding of the 17th ACM conference on Information and knowledge management, pages 469–478.
  3. R. A. Baeza-Yates, C. A. Hurtado, and M. Mendoza. (2004). “Query recommendation using query logs in search engines”. In EDBT Workshops, pages 588–596.
  4. D. Beeferman and A. Berger (2000). "Agglomerative clustering of a search engine query log”, In KDD.
  5. Paolo Boldi, Francesco Bonchi, Carlos Castillo (2009). “Query Suggestions Using Query-Flow Graphs”, WSCD ’09, Feb 9, 2009. In WSCD ’09: Proc. of the workshop on Web Search Click Data, pages 56–63, New York, NY, USA. ACM.
  6. Francesco Bonchi, Raffaele Perego, Fabrizio Silvestri (2011). “Recommendations for the Long Tail by Term-Query Graph”, WWW 2011.
  7. Rongwei Cen, Yiqun Liu, Min Zhang, Bo Zhou, Liyun Ru, Shaoping Ma (2009). “Exploring Relevance for Clicks”, CIKM’09.
  8. Z. Zhang and O. Nasraoui (2006). “Mining search engine query logs for query recommendation”, In WWW.
  9. J.-R. Wen, J.-Y. Nie and H.-J. Zhang (2001). “Clustering user queries of a search engine”, In Proceedings of WWW ’01, pages 162–168.
  10. M.Fern´andez-Fern´andez and D.Gayo Avello (2009). “Hierarchical taxonomy extraction by mining topical query sessions”, In KDIR.
  11. F. M. Suchanek, G. Kasneci, and G. Weikum (2007). “Yago: A core of semantic knowledge-unifying wordnet and Wikipedia”, In WWW.
  12. Sumit Bhatia, Debapriyo Majumdar, Prasenjit Mitra (2011).”Query Suggestions in the Absence of Query Logs”. SIGIR’11, July 24–28.
  13. D. Beeferman and A. Berger (2000). “Agglomerative clustering of a search engine query log”, In Proceedings of KDD’00, pages 407–416.
  14. T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. (2006) “Accurately interpreting click through data as implicit feedback”. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 154–161.
  15. H. Cui, J.-R.Wen, J.-Y. Nie, and W.-Y. Ma (2002). “Probabilistic query expansion using query logs”, In WWW ’02: Proceedings of the 11th international conference on World Wide Web, pages 325–332.
  16. Boldi P., Bonchi F., Castillo C., and Vigna, S (2008).”Query reformulation models and patterns”, Submitted for publication.
  17. Jones, R., and Klinkner, K. L (2008). “Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs”, In Conference on Information and Knowledge Management (CIKM) (October), ACM Press.
  18. Joachims, T., Granka, L., Pan, B., Hembrooke, H., Radlinski, F., and Gay.G (2007). “Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search”, ACM Trans. Inf. Syst. 25, 2 (Apr. 2007).
Index Terms

Computer Science
Information Sciences

Keywords

Query suggestion query log query session click through Web search query templates query recommendation