IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
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
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
The relationship between IR effectiveness measures and user satisfaction
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
Entropy-biased models for query representation on the click graph
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Smoothing clickthrough data for web search ranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Examining the generalizability of the User Engagement Scale (UES) in exploratory search
Information Processing and Management: an International Journal
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Click data captures many users' document preferences for a query and has been shown to help significantly improve search engine ranking. However, most click data is noisy and of low frequency, with queries associated to documents via only one or a few clicks. This severely limits the usefulness of click data as a ranking signal. Given potentially noisy clicks comprising results with at most one click for a query, how do we extract high-quality clicks that may be useful for ranking? In this poster, we introduce a technique based on query entropy for noise reduction in click data. We study the effect of query entropy and as well as features such as user engagement and the match between the query and the document. Based on query entropy plus other features, we can sample noisy data to 15% of its overall size with 43% query recall and an average increase of 20% in precision for recalled queries.