Study on Methods and Estimation of Location Aware Keyword Query Suggestion

Kattu Sujatha, J BalaAmbedkar

Abstract


We plan a location-aware keyword query suggestion. We propose a weighted watchword record diagram, which catches both the semantic pertinence between catchphrase inquiries and the spatial separation between the subsequent archives and the client area. The diagram is perused in an irregular stroll with-restart form, to choose the catchphrase inquiries with the most elevated scores as recommendations. To make our structure adaptable, we propose a segment based methodology that outflanks the pattern algorithm by up to a request of size. The suitability of our system and the execution of the algorithms are assessed utilizing genuine information

References


R. Baeza-Yates, C. Hurtado, and M. Mendoza, “Query recommendationusing query logs in search engines,” in Proc. Int. Conf. CurrentTrends Database Technol., 2004, pp. 588–596.

D. Beeferman and A. Berger, “Agglomerative clustering of asearch engine query log,” in Proc. 6th ACM SIGKDD Int. Conf.Knowl. Discovery Data Mining, 2000, pp. 407–416.

H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li,“Context-aware query suggestion by mining click-through andsession data,” in Proc. 14th ACM SIGKDD Int. Conf. Knowl. DiscoveryData Mining, 2008, pp. 875–883.

N. Craswell and M. Szummer, “Random walks on the clickgraph,” in Proc. 30th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf.Retrieval, 2007, pp. 239–246.

Q. Mei, D. Zhou, and K. Church, “Query suggestion using hittingtime,” in Proc. 17th ACM Conf. Inf. Knowl. Manage., 2008,

pp. 469–478.

Y. Song and L.-W. He, “Optimal rare query suggestion withimplicit user feedback,” in Proc. 19th Int. Conf. World Wide Web,2010, pp. 901–910.

T. Miyanishi and T. Sakai, “Time-aware structured query suggestion,”in Proc. 36th Int. ACM SIGIR Conf. Res. Develop.Inf.

Retrieval, 2013, pp. 809–812.

A. Anagnostopoulos, L. Becchetti, C. Castillo, and A. Gionis, “Anoptimization framework for query recommendation,” in Proc.ACM Int. Conf. Web Search Data Mining, 2010, pp. 161–170.

P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna,

“The query-flow graph: Model and applications,” in Proc. 17thACM Conf. Inf. Knowl.Manage., 2008, pp. 609–618.

Y. Song, D. Zhou, and L.-w.He, “Query suggestion by constructingterm-transition graphs,” in Proc. 5th ACM Int. Conf. Web SearchData Mining, 2012, pp. 353–362.

L. Li, G. Xu, Z. Yang, P. Dolog, Y. Zhang, and M. Kitsuregawa,

“An efficient approach to suggesting topically related web queriesusing hidden topic model,” World Wide Web, vol. 16, pp. 273–297,2013.

D. Wu, M. L. Yiu, and C. S. Jensen, “Moving spatial keywordqueries: Formulation, methods, and analysis,” ACM Trans. DatabaseSyst., vol. 38, no. 1, pp. 7:1–7:47, 2013.

D. Wu, G. Cong, and C. S. Jensen, “A framework for efficient spatialweb object retrieval,” VLDB J., vol. 21, no. 6, pp. 797–822, 2012.[14] J. Fan, G. Li, L. Zhou, S. Chen, and J. Hu, “SEAL: Spatio-textualsimilarity search,” Proc. VLDB Endowment, vol. 5, no. 9, pp. 824–835, 2012.

P. Bouros, S. Ge, and N. Mamoulis, “Spatio-textual similarityjoins,” Proc. VLDB Endowment, vol. 6, no. 1, pp. 1–12, 2012.


Full Text: PDF [Full Text]

Refbacks

  • There are currently no refbacks.


Copyright © 2013, All rights reserved.| ijseat.com

Creative Commons License
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.