Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Detecting online commercial intention (OCI)
Proceedings of the 15th international conference on World Wide Web
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Predicting bounce rates in sponsored search advertisements
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
In the Mood to Click? Towards Inferring Receptiveness to Search Advertising
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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An improved understanding of the relationship between search intent, result quality, and searcher behavior is crucial for improving the effectiveness of web search. While recent progress in user behavior mining has been largely focused on aggregate server-side click logs, we present a new search behavior model that incorporates fine-grained user interactions with the search results. We show that mining these interactions, such as mouse movements and scrolling, can enable more effective detection of the user's search intent. Potential applications include automatic search evaluation, improving search ranking, result presentation, and search advertising. As a case study, we report results on distinguishing between "research" and "purchase" variants of commercial intent, that show our method to be more effective than the current state-of-the-art.