Volume- 3
Issue- 2
Year- 2015
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Akash Dalvi , Yogesh Khajure, Dhanraj Patne, Kapil Waygand
For a wide subject and ambiguous query, diverse users may have distinctive search objectives when they submit it to a web search engine. The derivation and examination of user pursuit objectives i.e. goals can be extremely helpful in enhancing web search tool significance and user experience. In this, we propose a methodology to gather user look objectives by examining internet searcher query logs. In the first place, we propose a structure to find diverse user hunt objectives down a query by grouping i.e clustering the proposed feed-back sessions. Input sessions are developed from user navigate logs and can effectively reflect the data needs of users. Second, we propose a methodology to produce pseudo documents to better represent the feed-back sessions for grouping. At last, we propose another measure "Classified Average Precision (CAP)" to assess the execution of deriving user look objectives.
[1] Z. Lu, H. Zha, X. Yang, W. Lin, and Z. Zheng, “A New Algorithm for Inferring User Search Goals with Feedback Sessions”, pp. 502-513, 2013.
[2] 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.
[3] O. Zamir and O. Etzioni. Grouper: A dynamic clustering interface to web search results. Computer Networks, 31(11-16), pp.1361-1374, 1999.
[4] 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.
[5] X. Wang and C.-X Zhai, “Learn from Web Search Logs to Organize Search Results,” Proc. 30th Ann. Int’l ACMSIGIR Conf. Research and Development in Information Retrieval (SIGIR ’07), pp. 87-94, 2007.
[6] 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.
[7] 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.
[8] 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.
Computer Department, Pune University/ Trinity Academy of Engineering, Pune, India,
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