Discovery of Web Robot Sessions Based on their Navigational Patterns
Data Mining and Knowledge Discovery
LA-WEB '05 Proceedings of the Third Latin American Web Congress
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Characterizing typical and atypical user sessions in clickstreams
Proceedings of the 17th international conference on World Wide Web
Are click-through data adequate for learning web search rankings?
Proceedings of the 17th ACM conference on Information and knowledge management
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Mining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance evaluation and algorithm optimization. For commercial search engines, user click-through data contains useful information as well as large amount of inevitable noises. This paper proposes an approach to recognize reliable and meaningful user clicks (referred to as Relevant Clicks, RCs) in click-through data. By modeling user click-through behavior on search result lists, we propose several features to separate RCs from click noises. A learning algorithm is presented to estimate the quality of user clicks. Experimental results on large scale dataset show that: 1) our model effectively identifies RCs in noisy click-through data; 2) Different from previous click-through analysis efforts, our approach works well for both hot queries and long-tail queries.