Information Retrieval
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
Feature congestion: a measure of display clutter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Usability tool for analysis of web designs using mouse tracks
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Proceedings of the 15th international conference on World Wide Web
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Eye-mouse coordination patterns on web search results pages
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Exploring mouse movements for inferring query intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
To swing or not to swing: learning when (not) to advertise
Proceedings of the 17th ACM conference on Information and knowledge management
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
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Feature hashing for large scale multitask learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Good abandonment in mobile and PC internet search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Sources of evidence for vertical selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
Inferring search behaviors using partially observable Markov (POM) model
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
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
Do predictions of visual perception aid design?
ACM Transactions on Applied Perception (TAP)
The anatomy of a click: modeling user behavior on web information systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Inferring search behaviors using partially observable markov model with duration (POMD)
Proceedings of the fourth ACM international conference on Web search and data mining
No clicks, no problem: using cursor movements to understand and improve search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using gaze patterns to study and predict reading struggles due to distraction
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Creating personalized digital human models of perception for visual analytics
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
On saliency, affect and focused attention
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
User see, user point: gaze and cursor alignment in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mouse tracking: measuring and predicting users' experience of web-based content
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning to suggest: a machine learning framework for ranking query suggestions
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Improving searcher models using mouse cursor activity
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Incorporating vertical results into search click models
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts
Proceedings of the 22nd international conference on World Wide Web
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Understanding how users examine result pages across a broad range of information needs is critical for search engine design. Cursor movements can be used to estimate visual attention on search engine results page (SERP) components, including traditional snippets, aggregated results, and advertisements. However, these signals can only be leveraged for SERPs where cursor tracking was enabled, limiting their utility for informing the design of new SERPs. In this work, we develop robust, log-based mouse movement models capable of estimating searcher attention on novel SERP arrangements. These models can help improve SERP design by anticipating searchers' engagement patterns given a proposed arrangement. We demonstrate the efficacy of our method using a large set of mouse-tracking data collected from two independent commercial search engines.