Proceedings of the 6th international conference on Intelligent user interfaces
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Design and Analysis of Experiments
Design and Analysis of Experiments
How Users Perceive and Appraise Personalized Recommendations
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
A fully automated recommender system using collaborative filters
CIIT '07 The Sixth IASTED International Conference on Communications, Internet, and Information Technology
No clicks, no problem: using cursor movements to understand and improve search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Categorize web sites based on design issues
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: users and applications - Volume Part IV
User see, user point: gaze and cursor alignment in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic web design refinements based on collective user behavior
CHI '12 Extended Abstracts 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
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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Explicit relevance feedback involves explicit ratings of documents or terms by users and disrupts their browsing and searching. The alternative non-disruptive method is implicit feedback inferring users' needs and interests by monitoring their regular interaction with the system. Some implicit indicators of interest, such as reading time, have been investigated in previous studies and were found indicative to the relevance of documents but not sufficiently accurate [1,2,3,4]. In this paper we present and examine several new relative implicit feedback indicators, and study the effect of combining several implicit indicators. The paper describes a large-scale user study on which users' searches were observed by a specially developed browser that recorded their behavior (implicit indicators) as well as their explicit ratings. We analyzed the relationship between implicit indicators and explicit ratings and found that a certain combination of implicit indicators achieved higher correlation with the explicit ratings than any of the individual indicators. We have also found that the relative indicators are more indicative to the level of interest of a user item than the non-relative indicators.