C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial Intelligence
ACM SIGIR Forum
What's new on the web?: the evolution of the web from a search engine perspective
Proceedings of the 13th international conference on World Wide Web
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
LA-WEB '05 Proceedings of the Third Latin American Web Congress
WebKDD 2006: web mining and web usage analysis post-workshop report
ACM SIGKDD Explorations Newsletter
Survey and evaluation of query intent detection methods
Proceedings of the 2009 workshop on Web Search Click Data
Estimating Ad Clickthrough Rate through Query Intent Analysis
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Characterizing commercial intent
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the 18th ACM conference on Information and knowledge management
Classifying web search queries to identify high revenue generating customers
Journal of the American Society for Information Science and Technology
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The identification of a user's intention or interest by the analysis of the queries submitted to a search engine and the documents selected as answers to these queries, can be very useful to offer more adequate results for that user. In this Chapter we present the analysis of a Web search engine query log from two different perspectives: the query session and the clicked document. In the first perspective, that of the query session, we process and analyze web search engine query and click data for the query session (query + clicked results) conducted by the user. We initially state some hypotheses for possible user types and quality profiles for the user session, based on descriptive variables of the session. In the second perspective, that of the clicked document, we repeat the process from the perspective of the documents (URL's) selected. We also initially define possible document categories and select descriptive variables to define the documents. We apply a systematic data mining process to click data, contrasting non- supervised (Kohonen) and supervised (C4.5) methods to cluster and model the data, in order to identify profiles and rules which relate to theoretical user behavior and user session "quality", from the point of view of user session, and to identify document profiles which relate to theoretical user behavior, and document (URL) organization, from the document perspective.