Optimizing search engines using clickthrough data
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Implicit feedback for inferring user preference: a bibliography
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Optimizing web search using web click-through data
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Evaluating implicit measures to improve web search
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A study of factors affecting the utility of implicit relevance feedback
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Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query chains: learning to rank from implicit feedback
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SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Minimally invasive randomization for collecting unbiased preferences from clickthrough logs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
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
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In the Mood to Click? Towards Inferring Receptiveness to Search Advertising
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Inferring search behaviors using partially observable Markov (POM) model
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Large-scale validation and analysis of interleaved search evaluation
ACM Transactions on Information Systems (TOIS)
A noise-aware click model for web search
Proceedings of the fifth ACM international conference on Web search and data mining
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In this paper, we develop and evaluate several probabilistic models of user click-through behavior that are appropriate for modeling the click-through rates of items that are presented to the user in a list. Potential applications include modeling the click-through rates of search results from a search engine, items ranked by a recommendation system, and search advertisements returned by a search engine. Our models capture contextual factors related to the presentation as well as the underlying relevance or quality of the item. We focus on two types of contextual factors for a given item; the positional context of the item and the quality of the other results. We evaluate our models on a search advertising dataset from Microsoft's Live search engine and demonstrate that modeling contextual factors improves the accuracy of click-through models.