Learning users' interests by unobtrusively observing their normal behavior
Proceedings of the 5th international conference on Intelligent user interfaces
Proceedings of the 6th international conference on Intelligent user interfaces
Cheese: tracking mouse movement activity on websites, a tool for user modeling
CHI '01 Extended Abstracts on Human Factors in Computing Systems
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
Implicit user profiling for on demand relevance feedback
Proceedings of the 9th international conference on Intelligent user interfaces
Eye-mouse coordination patterns on web search results pages
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Towards Inferring Sequential-Global Dimension of Learning Styles from Mouse Movement Patterns
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
IEICE - Transactions on Information and Systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
User see, user point: gaze and cursor alignment in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Web browsing behavior analysis and interactive hypervideo
ACM Transactions on the Web (TWEB)
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The main source of information in most adaptive hypermedia systems are server monitored events such as page visits and link selections. One drawback of this approach is that pages are treated as "monolithic" entities, since the system cannot determine what portions may have drawn the user's attention. Departing from this model, the work described here demonstrates that client-side monitoring and interpretation of users' interactive behavior (such as mouse moves, clicks and scrolling) allows for detailed and significantly accurate predictions on what sections of a page have been looked at. More specifically, this paper provides a detailed description of an algorithm developed to predict which paragraphs of text in a hypertext document have been read, and to which extent. It also describes the user study, involving eye-tracking for baseline comparison, that served as the basis for the algorithm.