Computational models of information scent-following in a very large browsable text collection
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A comparison of LSA, wordNet and PMI-IR for predicting user click behavior
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
TreePlus: Interactive Exploration of Networks with Enhanced Tree Layouts
IEEE Transactions on Visualization and Computer Graphics
SNIF-ACT: a model of information foraging on the world wide web
UM'03 Proceedings of the 9th international conference on User modeling
A computational model for human eye-movements in military simulations
Computational & Mathematical Organization Theory
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Semantic Relevancy Maps are a visual analytic technique for representing the distribution of semantic relevancy across an information display. The maps highlight the text areas of the display corresponding to the relevance of that text to user goals, with stronger highlights indicating higher degrees of relevance. Semantic Relevancy Maps were developed as a tool for high-fidelity computational cognitive models that search complex information displays in the same manner as humans. However, they offer the potential to be a standalone tool for quickly evaluating the spatial layout of information for designers or, more simply, for identifying the spatial location of sought-for information by any computer user.