A learning agent for wireless news access
Proceedings of the 5th international conference on Intelligent user interfaces
Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Learning users' interests by unobtrusively observing their normal behavior
Proceedings of the 5th international conference on Intelligent user interfaces
A reinforcement learning agent for personalized information filtering
Proceedings of the 5th international conference on Intelligent user interfaces
Proceedings of the 6th international conference on Intelligent user interfaces
A writer's collaborative assistant
Proceedings of the 7th international conference on Intelligent user interfaces
Implicit user profiling for on demand relevance feedback
Proceedings of the 9th international conference on Intelligent user interfaces
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
SAS for Mixed Models, Second Edition
SAS for Mixed Models, Second Edition
Information Processing and Management: an International Journal
Improved recommendation based on collaborative tagging behaviors
Proceedings of the 13th international conference on Intelligent user interfaces
Learning to learn implicit queries from gaze patterns
Proceedings of the 25th international conference on Machine learning
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
Can eyes reveal interest? Implicit queries from gaze patterns
User Modeling and User-Adapted Interaction
Implicit relevance feedback from eye movements
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Attentive documents: Eye tracking as implicit feedback for information retrieval and beyond
ACM Transactions on Interactive Intelligent Systems (TiiS)
Exploring gaze data for determining user learning with an interactive simulation
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Towards inferring language expertise using eye tracking
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Does interactive search results overview help?: an eye tracking study
CHI '13 Extended Abstracts on Human Factors in Computing Systems
My reading life: towards utilizing eyetracking on unmodified tablets and phones
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Effects of working memory capacity on users' search effort
Proceedings of the International Conference on Multimedia, Interaction, Design and Innovation
Inferring user knowledge level from eye movement patterns
Information Processing and Management: an International Journal
Adaptive visualization for exploratory information retrieval
Information Processing and Management: an International Journal
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Reading is one of the most important skills in today's society. The ubiquity of this activity has naturally affected many information systems; the only goal of some is the presentation of textual information. One concrete task often performed on a computer and involving reading is finding relevant parts of text. In the current study, we investigated if word-level relevance, defined as a binary measure of an individual word being congruent with the reader's current informational needs, could be inferred given only the text and eye movements of readers. We found that the number of fixations, first-pass fixations, and the total viewing time can be used to predict the relevance of sentence-terminal words. In light of what is known about eye movements of readers, knowing which sentence-terminal words are relevant can help in an unobtrusive identification of relevant sentences.