Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Predicting short-term interests using activity-based search context
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Inferring and using location metadata to personalize web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Large-scale analysis of individual and task differences in search result page examination strategies
Proceedings of the fifth ACM international conference on Web search and data mining
Predicting web search success with fine-grained interaction data
Proceedings of the 21st ACM international conference on Information and knowledge management
Improving search result summaries by using searcher behavior data
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Mining touch interaction data on mobile devices to predict web search result relevance
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Captions and biases in diagnostic search
ACM Transactions on the Web (TWEB)
Discovering common motifs in cursor movement data for improving web search
Proceedings of the 7th ACM international conference on Web search and data mining
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Users' search activity has been used as implicit feedback to model search interests and improve the performance of search systems. In search engines, this behavior usually takes the form of queries and result clicks. However, richer data on how people engage with search results can now be captured at scale, creating new opportu-nities to enhance search. In this poster we focus on one type of newly-observable behavior: text selection events on search-result captions. We show that we can use text selections as implicit feedback to significantly improve search result relevance.