Machine Learning
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
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Usability tool for analysis of web designs using mouse tracks
CHI '06 Extended Abstracts on Human Factors in Computing Systems
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Efficient multiple-click models in web search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
BBM: bayesian browsing model from petabyte-scale data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Proceedings of the 19th international conference on World wide web
User browsing models: relevance versus examination
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A characterization of the layout definition problem for web search results
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems
Addressing people's information needs directly in a web search result page
Proceedings of the 20th international conference on World wide web
No clicks, no problem: using cursor movements to understand and improve search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ViewSer: enabling large-scale remote user studies of web search examination and interaction
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Detecting success in mobile search from interaction
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Proceedings of the 21st international conference on World Wide Web
Mouse tracking: measuring and predicting users' experience of web-based content
Proceedings of the SIGCHI Conference 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
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It is crucial for the success of a search-driven web application to answer users' queries in the best possible way. A common approach is to use click models for guessing the relevance of search results. However, these models are imprecise and waive valuable information one can gain from non-click user interactions. We introduce TellMyRelevance!---a novel automatic end-to-end pipeline for tracking cursor interactions at the client, analyzing these and learning according relevance models. Yet, the models depend on the layout of the search results page involved, which makes them difficult to evaluate and compare. Thus, we use a Random Mouse Cursor as an extension to our pipeline for generating layout-dependent baselines. Based on these, we can perform evaluations of real-world relevance models. A large-scale interaction log analysis showed that we can learn relevance models whose predictions compare favorably to predictions of an existing state-of-the-art click model.