Agglomerative clustering of a search engine query log
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Clustering user queries of a search engine
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Optimizing search engines using clickthrough data
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Implicit feedback for inferring user preference: a bibliography
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The determinants of web page viewing behavior: an eye-tracking study
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Depth- and breadth-first processing of search result lists
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Evaluating implicit measures to improve web search
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Query chains: learning to rank from implicit feedback
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Learning user interaction models for predicting web search result preferences
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Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
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What are you looking for?: an eye-tracking study of information usage in web search
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Predicting clicks: estimating the click-through rate for new ads
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Are people biased in their use of search engines?
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Eye tracking and online search: Lessons learned and challenges ahead
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Contextual advertising by combining relevance with click feedback
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A user browsing model to predict search engine click data from past observations.
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BrowseRank: letting web users vote for page importance
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How does clickthrough data reflect retrieval quality?
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Click chain model in web search
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PSkip: estimating relevance ranking quality from web search clickthrough data
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Optimizing two-dimensional search results presentation
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Inferring search behaviors using partially observable markov model with duration (POMD)
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This article describes an application of the partially observable Markov (POM) model to the analysis of a large scale commercial web search log. Mathematically, POM is a variant of the hidden Markov model in which all the hidden state transitions do not necessarily emit observable events. This property of POM is used to model, as the hidden process, a common search behavior that users would read and skip search results, leaving no observable user actions to record in the search logs. The Markov nature of the model further lends support to cope with the facts that a single observed sequence can be probabilistically associated with many hidden sequences that have variable lengths, and the search results can be read in various temporal orders that are not necessarily reflected in the observed sequence of user actions. To tackle the implementation challenges accompanying the flexibility and analytic powers of POM, we introduce segmental Viterbi algorithm based on segmental decoding and Viterbi training to train the POM model parameters and apply them to uncover hidden processes from the search logs. To validate the model, the latent variables modeling the browsing patterns on the search result page are compared with the experimental data of the eye tracking stu-dies. The close agreements suggest that the search logs do contain rich information of user behaviors in browsing the search result page even though they are not directly observable, and that using POM to understand these sophisticated search behaviors is a promising approach.