Cheese: tracking mouse movement activity on websites, a tool for user modeling
CHI '01 Extended Abstracts on Human Factors in Computing Systems
Proceedings of the 15th international conference on World Wide Web
Identifying "best bet" web search results by mining past user behavior
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Catching the drift: learning broad matches from clickthrough data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Intent-Based Categorization of Search Results Using Questions from Web Q&A Corpus
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Towards predicting web searcher gaze position from mouse movements
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Assessing users' interactions for clustering web documents: a pragmatic approach
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Ready to buy or just browsing?: detecting web searcher goals from interaction data
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
Pseudo test collections for learning web search ranking functions
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Detecting Intent of Web Queries Using Questions and Answers in CQA Corpus
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Intent feature discovery using Q&A corpus and web data
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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
Unsupervised extraction of template structure in web search queries
Proceedings of the 21st international conference on World Wide Web
Proceedings of the 21st international conference on World Wide Web
User see, user point: gaze and cursor alignment in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improving searcher models using mouse cursor activity
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Usage data in web search: benefits and limitations
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database 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
Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts
Proceedings of the 22nd international conference on World Wide Web
Robust models of mouse movement on dynamic web search results pages
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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Clickthrough on search results have been successfully used to infer user interest and preferences, but are often noisy and potentially ambiguous. We explore the potential of a complementary, more sensitive signal -mouse movements- in providing insights into the intent behind a web search query. We report preliminary results of studying user mouse movements on search result pages, with the goal of inferring user intent - in particular, to explore whether we can automatically distinguish the different query classes such as navigational vs. informational. Our preliminary exploration confirms the value of studying mouse movements for user intent inference, and suggests interesting avenues for future exploration.