Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Eye-mouse coordination patterns on web search results pages
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Towards predicting web searcher gaze position from mouse movements
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Predicting searcher frustration
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Assessing the scenic route: measuring the value of search trails in web logs
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
No clicks, no problem: using cursor movements to understand and improve search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Why searchers switch: understanding and predicting engine switching rationales
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Find it if you can: a game for modeling different types of web search success using interaction data
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
ViewSer: enabling large-scale remote user studies of web search examination and interaction
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Proceedings of the 21st international conference on World Wide Web
Improving searcher models using mouse cursor activity
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Text selections as implicit relevance feedback
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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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Detecting and predicting searcher success is essential for automatically evaluating and improving Web search engine performance. In the past, Web searcher behavior data, such as result clickthrough, dwell time, and query reformulation sequences, have been successfully used for a variety of tasks, including prediction of success in a search session. However, the effectiveness of the previous approaches has been limited, as they tend to ignore how searchers actually view and interact with the visited pages. We show that fine-grained interactions, such as mouse cursor movements and scrolling, provide additional clues for better predicting success of a search session as a whole. To this end, we identify patterns of examination and interaction behavior that correspond to search success, and design a new Fine-grained Session Behavior (FSB) model to capture these patterns. Our experimental results show that FSB is significantly more effective than the state-of-the-art approaches that do not use these additional interaction data.