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Inferring User Search using Feedback Session and Semantic Search
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 170 - Number 7 |
| Year of Publication: 2017 |
| Authors: Deshmukh N. M., Patil B. M., Chandode V. M. |
10.5120/ijca2017914921
|
Deshmukh N. M., Patil B. M., Chandode V. M. . Inferring User Search using Feedback Session and Semantic Search. International Journal of Computer Applications. 170, 7 ( Jul 2017), 31-35. DOI=10.5120/ijca2017914921
Abstract
Different users may have diverse search goals for ambiguous queries when they put forward it to a search engine. Search engine relevance and user experience are enhanced by the analysis of user search goals. In this work, a novel approach has been proposed to infer user search goals for a query by clustering its feedback sessions represented by pseudo-documents. First, goals of particular queries by clustering feedback session are found which depends upon user click through logs. It can efficiently replicate goals of users. Secondly, to estimate query texts in user minds, clustered feedback session is mapped to pseudo documents. Then ‘CAP’ (Classified Average Precision) is used to appraise performance of user search goals. Finally, a new criterion is “Semantic code” is proposed in which one can find out a particular aim of user’s search.
References
- Zheng Lu, Hongyuan Zha, Xiaokang Yang, Weiyao Lin and Zhaohui Zheng,A new algorithm inferring user search goals using feedback session, IEEE transaction on knowledge and data engineering, vol. 25, no. 3, march 2013
- 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 login search Engines,” Proc. Int’l Conf. Current Trend in Database Technology (EDBT’ 04), pp. 2004.
- D. Beeferman and A. Berger,“Agglomerative Clustering of a Search Engine QueryLog,” Proc.Sixth ACMSIGKDD 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 we 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 SIGKD 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. 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.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 ClicK through 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. 699-00
- 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.
- M. Pasca and B.-V Durme, “What You Seek Is what You Get: Extraction of Class Attributes from Query Logs,” Proc. 20th Int’l Joint Conf Artificial Intelligence (IJCAI ’07), pp. 2007.
- B. Poblete and B.-Y Ricardo, “Query-Sets Using Implicit Feedback and Query Patterns to Organize Web Documents,” Proc. 17th Int’lConf. World Wide Web (WWW ’08), pp. 2008.
- D. Shen, J. Sun, Q. Yang, and Z. Chen, “Building Bridges for Web QueryClassi-fication,” Proc. 29th Ann. Int’l ACM SIGI Conf. Research and Development in Information Retrieval (SIGIR ’06), pp. 131-138, 2006.
- X. Wang and C.-X Zhai, “Learn from Web Search Logs to Organize Search Results,” Proc.30th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR’07), pp. 87-94, 2007.
- J.-R Wen, J.-Y Nie, and H.-J Zhang, “Clustering User Queries of a Search Engine,” Proc. Tenth Int’l Conf. World Wide Web (WWW ’01), pp. 162-168, 2001.
- H.-J Zeng, Q.-C He, Z. Chen, W.-Y Ma, and J.Ma, “Learning to Cluster Web Search Results, Proc. 27th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’04), pp. 210-217, 2004.
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