SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Finding Similar Queries to Satisfy Searches Based on Query Traces
OOIS '02 Proceedings of the Workshops on Advances in Object-Oriented Information Systems
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Recommending better queries from click-through data
SPIRE'05 Proceedings of the 12th international conference on String Processing and 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
Efficient multiple-click models in web search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Post-rank reordering: resolving preference misalignments between search engines and end users
Proceedings of the 18th ACM conference on Information and knowledge management
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Query logs record past query sessions across a time span. A statistical model is proposed to explain the log generation process. Within a search engine list of results, the model explains the document selection – a user’s click – by taking into account both a document position and its popularity. We show that it is possible to quantify this influence and consequently estimate document “un-biased” popularities. Among other applications, this allows to re-order the result list to match more closely user preferences and to use the logs as a feedback to improve search engines.