Results from the first World-Wide Web user survey
Selected papers of the first conference on World-Wide Web
Analysis of a very large web search engine query log
ACM SIGIR Forum
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGIR Forum
Understanding user goals in web search
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
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
How does clickthrough data reflect retrieval quality?
Proceedings of the 17th ACM conference on Information and knowledge management
Efficient multiple-click models in web search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Click chain model in web search
Proceedings of the 18th international conference on World wide web
BBM: bayesian browsing model from petabyte-scale data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Bayesian Browsing Model: Exact Inference of Document Relevance from Petabyte-Scale Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
The anatomy of a click: modeling user behavior on web information systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning to re-rank: query-dependent image re-ranking using click data
Proceedings of the 20th international conference on World wide web
Balancing exploration and exploitation in learning to rank online
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
A probabilistic method for inferring preferences from clicks
Proceedings of the 20th ACM international conference on Information and knowledge management
On caption bias in interleaving experiments
Proceedings of the 21st ACM international conference on Information and knowledge management
Lerot: an online learning to rank framework
Proceedings of the 2013 workshop on Living labs for information retrieval evaluation
Relative confidence sampling for efficient on-line ranker evaluation
Proceedings of the 7th ACM international conference on Web search and data mining
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Click models provide a principled way of understanding user interaction with web search results in a query session and a statistical tool for leveraging search engine click logs to analyze and improve user experience. An important component in all existing click models is the user behavior assumption -- how users scan, examine and click web documents listed in the result page. Usually the average user behavior pattern is summarized in a small set of global parameters. Can we fit multiple models with different user behavior parameters on a click data set? A previous study showed that the mixture modeling approach did not lead to better performance despite extra computational cost. In this paper, we present how to tailor click models to user goals in web search through query term classification. We demonstrate that better predicative power could be achieved by fitting two click models for navigational queries and informational queries respectively, as evidenced by the likelihood and perplexity evaluation results on a subset of the MSN 2006 RFP data which consists of 121,179 distinct query terms and over 2.8 million query sessions. We also propose search relevance score (SRS) as a flexible evaluation metric of search engine performance. This metric can be derived as summary statistics under any click model, and is applicable to a single query session, a particular query term and the search engine overall.