What can a mouse cursor tell us more?: correlation of eye/mouse movements on web browsing
CHI '01 Extended Abstracts on Human Factors in Computing Systems
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGIR Forum
What are you looking for?: an eye-tracking study of information usage in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Eye-mouse coordination patterns on web search results pages
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Query expansion using gaze-based feedback on the subdocument level
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
User-oriented document summarization through vision-based eye-tracking
Proceedings of the 14th international conference on Intelligent user interfaces
What do you see when you're surfing?: using eye tracking to predict salient regions of web pages
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Segment-level display time as implicit feedback: a comparison to eye tracking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
From "Dango" to "Japanese Cakes": Query Reformulation Models and Patterns
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Analyzing and evaluating query reformulation strategies in web search logs
Proceedings of the 18th ACM conference on Information and knowledge management
Characterizing and predicting search engine switching behavior
Proceedings of the 18th ACM conference on Information and knowledge management
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
Actively predicting diverse search intent from user browsing behaviors
Proceedings of the 19th international conference on World wide web
Predicting searcher frustration
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
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
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Proceedings of the 21st international conference on World Wide Web
Are web users really Markovian?
Proceedings of the 21st international conference on World Wide Web
Search intent estimation from user's eye movements for supporting information seeking
Proceedings of the International Working Conference on Advanced Visual Interfaces
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This paper proposes a method to discover how a user's search intent changes using his/her behavior during a Web search. A Web search user has a particular search intent and formulates search queries according to that intent. It is, however, a difficult task for the user to formulate a optimal query, a single query able to find documents which completely satisfy his/her information need, by himself. After issuing the initial query, the user usually examines the search results, and modifies his/her initial query. The subsequent queries may be a query of Specialization type, Parallel Move type and so on. By recording these subsequent queries and the corresponding user behavior (including eye-gazing behavior), the present work tries to find the relationship between the user's query reformulation and user's behavior. The proposed method constructs a SVM classifier from the behavior log data obtained from the search and browsing processes. Our experimental results show that the proposed method can classify the next query reformulation into five categories using only the current search behavior data with about 41 % accuracy, greater than the baseline methods. We also analyze which and to what extent the user's behavior data is useful for predicting query reformulations.