Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Click chain model in web search
Proceedings of the 18th international conference on World wide web
Towards a model of implicit feedback for Web search
Journal of the American Society for Information Science and Technology
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Click logs from search engines provide a rich opportunity to acquire implicit feedback from users. Patterns derived from the time between a posted query and a click provide information on the ranking quality, reflecting the perceived relevance of a retrieved URL. This paper applies the Kaplan-Meier estimator to study click patterns. The visualization of click curves demonstrates the interaction between the relevance and the rank position of URLs. The observed results demonstrate the potential of using click curves to predict the quality of the top-ranked results.