A NARRATIVE LEARNING TECHNIQUE IN SEARCH ENGINE USING FEEDBACK SESSIONS FOR USERS SEARCH GOALS

Venkata V S K Varma K, Venkata Phanikrishna B, D. D. D. Suribabu

Abstract


The new learning process is for reducing the unwanted results in the search from the search-engine. This can be achieved through gathering feedback sessions. For this we propose a new approach to infer user search goals by analyzing search engine query logs. Initially we find multiple user search intensions; secondly we propose a novel framework to clustering the user feedback sessions. These feedbacks are gathered by click through logs. We use pseudo-documents for clustering these collected user feedbacks. Externally we use a new stranded Classified Common Accuracy (CCA) for evaluating the performance of inferring users search goals. This will saves time and cut down the process costs.


Keywords


Search Engine; feedback sessions; query logs; clustering; pseudo documents and Classified Common Accuracy (CCA)

References


R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. ACM Press, 1999.

R. Baeza-Yates, C. Hurtado, and M. Mendoza, “Query Recommendation Using Query Logs in Search Engines,” Proc. Int’l Conf. Current Trends in Database Technology (EDBT ’04), pp. 588-596, 2004.

D. Beeferman and A. Berger, “Agglomerative Clustering of a Search Engine Query Log,” Proc. Sixth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’00), pp. 407-416, 2000.

S. Beitzel, E. Jensen, A. Chowdhury, and O. Frieder, “Varying Approaches to Topical Web Query Classification,” Proc. 30th Ann. Int’l ACM SIGIR Conf. Research and Development (SIGIR ’07), pp. 783-784, 2007.

H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li, “Context-Aware Query Suggestion by Mining Click-Through,” Proc. 14th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’08), pp. 875-883, 2008.

H. Chen and S. Dumais, “Bringing Order to the Web: Automatically Categorizing Search Results,” Proc. SIGCHI Conf. Human Factors in Computing Systems (SIGCHI ’00), pp. 145-152, 2000.

C.-K Huang, L.-F Chien, and Y.-J Oyang, “Relevant Term Suggestion in Interactive Web Search Based on Contextual Information in Query Session Logs,” J. Am. Soc. for Information Science and Technology, vol. 54, no. 7, pp. 638-649, 2003.

T. Joachims, “Evaluating Retrieval Performance Using Clickthrough Data,” Text Mining, J. Franke, G. Nakhaeizadeh, and I. Renz, eds., pp. 79-96, Physica/Springer Verlag, 2003.

T. Joachims, “Optimizing Search Engines Using Clickthrough Data,” Proc. Eighth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD ’02), pp. 133-142, 2002.

T. Joachims, L. Granka, B. Pang, H. Hembrooke, and G. Gay, “Accurately Interpreting Clickthrough Data as Implicit Feedback,” Proc. 28th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’05), pp. 154-161, 2005.

R. Jones and K.L. Klinkner, “Beyond the Session Timeout: Automatic Hierarchical Segmentation of Search Topics in Query Logs,” Proc. 17th ACM Conf. Information and Knowledge Management (CIKM ’08), pp. 699-708, 2008.

R. Jones, B. Rey, O. Madani, and W. Greiner, “Generating Query Substitutions,” Proc. 15th Int’l Conf. World Wide Web (WWW ’06), pp. 387-396, 2006.

U. Lee, Z. Liu, and J. Cho, “Automatic Identification of User Goals in Web Search,” Proc. 14th Int’l Conf. World Wide Web (WWW ’05), pp. 391-400, 2005.

X. Li, Y.-Y Wang, and A. Acero, “Learning Query Intent from Regularized Click Graphs,” Proc. 31st Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’08), pp. 339-346, 2008.

R. Jones and K.L. Klinkner, “Beyond the Session Timeout: Automatic Hierarchical Segmentation of Search Topics in Query Logs,” Proc. 17th ACM Conf. Information and Knowledge Management (CIKM ’08), pp. 699-708, 2008.

L. Barness, J. Opitz, and E. Gilbert-Barness, “Obesity: genetic, molecular, and environmental aspects,” American Journal of Medical Genetics Part A, vol. 143, no. 24, pp. 3016– 3034, 2007.

U. Lee, Z. Liu, and J. Cho, “Automatic Identification of User Goals in Web Search,” Proc. 14th Int’l Conf. World Wide Web (WWW ’05), pp. 391-400, 2005.

J. Evans and A. Rzhetsky, “Machine science,” Science, vol. 329, no.5990, p. 399, 2010.

T. Parsons, C. Power, S. Logan, and C. Summerbell, “Childhood predictors of adult obesity: a systematic review.” International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity, vol. 23, p. S1, 1999.


Refbacks

  • There are currently no refbacks.




Copyright © 2012 - 2023, All rights reserved.| ijitr.com

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