A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
The determinants of web page viewing behavior: an eye-tracking study
Proceedings of the 2004 symposium on Eye tracking research & applications
Depth- and breadth-first processing of search result lists
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
An eye tracking study of the effect of target rank on web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Web projections: learning from contextual subgraphs of the web
Proceedings of the 16th international conference on World Wide Web
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th 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
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Tailoring click models to user goals
Proceedings of the 2009 workshop on Web Search Click Data
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Click chain model in web search
Proceedings of the 18th international conference on World wide web
Models of searching and browsing: languages, studies, and applications
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Inferring search behaviors using partially observable Markov (POM) model
Proceedings of the third ACM international conference on Web search and data mining
Unsupervised extraction of template structure in web search queries
Proceedings of the 21st international conference on World Wide Web
Robust models of mouse movement on dynamic web search results pages
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The ultimate goal of information retrieval science continues to be providing relevant information to users while placing minimal cognitive load on them. The retrieval and presentation of relevant information (say, search results) as well as any dynamic system behavior (e.g., search engine re-ranking) depends acutely on estimating user intent. Hence, it is critical to use all the available information about user behavior at any stage of a search-session to accurately infer the user intent. However, the simplistic interfaces provided by search engines in order to minimize the user cognitive effort, and intrinsic limits imposed by privacy concerns, latency requirements, and other web instrumentation challenges, result in only a subset of user actions that are predictive of the search intent being captured. In this paper, we present a dynamic Bayesian network (DBN) that models user interaction with general web information systems, taking into account both observed (clicks etc.) as well as hidden (result examinations etc.) user actions. Our model goes beyond the ranked list information access paradigm and gives a solution where arbitrary context information can be incorporated in a principled fashion. To account for heterogeneity in user behavior as well as information access tasks, we further propose a bi-clustering algorithm that partitions users and tasks, and learns separate models for each bicluster. We instantiate this general DBN model for a typical static search interface comprising of a single query box and a ranked list of search results using a set of seven common user actions and various predictive state attributes. Experimental results on real-world web search log data indicate that one can obtain superior predictive performance on various session properties (such as click positions and reformulations) compared to simpler instantiations of the DBN.