Inferring User Search Goals with Feedback Sessions using K-means clustering algorithm
Dasari Amarendra, Kaveti Kiran Kumar, , ,
Affiliations M.Tech (CSE), Department of Computer Science & Engineering, NRI Institute of TechnologyAssistant Professor, Department of Computer Science & Engineering, NRI Institute of Technology.
Recognizing or inferring client's search objective from given query is a difficult job as search engines let users to
identify queries simply as a list of keywords which might refer to broad topics, to technical terminology, or even to proper nouns
that can be used to guide the search procedure to the significant compilation of documents. In order needs of users are
correspond to by queries submitted to search engines and different users have different search goals for a broad topic.
Sometimes queries may not exactly represent the user's information needs due to the use of short queries with uncertain terms.
thus to get the best results it is necessary to capture different user search goals. These user goals are nothing but information
on different aspects of a query that different users want to obtain. The judgment and analysis of user search goals can be
improved by the relevant result obtained from search engine and user's feedback. Here, feedback sessions are used to discover
different user search goals based on series of both clicked and unclicked URL's. The pseudo-documents are generated to better
represent feedback sessions which can reflect the information need of user. With this the original search results are restructured
and to evaluate the performance of restructured search results, classified average precision (CAP) is used. This evaluation is
used as feedback to select the optimal user search goals.
Dasari Amarendra,Kaveti Kiran Kumar."Inferring User Search Goals with Feedback Sessions using K-means clustering algorithm". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 11,pp.780-784, November- 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I1117.pdf,
Keywords : User search goals, feedback sessions, pseudo-documents, restructuring search results, classified average
 Zheng Lu, Hongyuan Zha, Xiaokang Yang, Weiyao Lin, Zhaohui Zheng, ‚A New Algorithm for Inferring User Search Goals with Feedback Sessions‛,IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 3, MARCH 2013
 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.
 D. Shen, J. Sun, Q. Yang, and Z. Chen, "Building Bridges for Web Query Classification", Proc. 29th Ann. Int'l ACM SIGIR 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.
 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.
 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.
 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.
 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.