Optimizing search engines using clickthrough data
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ACM SIGIR Forum
Eye-tracking analysis of user behavior in WWW search
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Accurately interpreting clickthrough data as implicit feedback
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Learning user interaction models for predicting web search result preferences
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Improving web search ranking by incorporating user behavior information
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Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Investigating behavioral variability in web search
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An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Modeling contextual factors of click rates
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Models of searching and browsing: languages, studies, and applications
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A statistical model of query log generation
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Efficient multiple-click models in web search
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Integration of news content into web results
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Analysis of long queries in a large scale search log
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Tailoring click models to user goals
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A dynamic bayesian network click model for web search ranking
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Click chain model in web search
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Identification of factors predicting clickthrough in Web searching using neural network analysis
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Interactively optimizing information retrieval systems as a dueling bandits problem
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PSkip: estimating relevance ranking quality from web search clickthrough data
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OLAP on search logs: an infrastructure supporting data-driven applications in search engines
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Global ranking by exploiting user clicks
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Entropy-biased models for query representation on the click graph
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Information Technology and Management
Post-rank reordering: resolving preference misalignments between search engines and end users
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Mining search engine clickthrough log for matching N-gram features
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A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
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Inferring search behaviors using partially observable Markov (POM) model
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A novel click model and its applications to online advertising
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Competing for users' attention: on the interplay between organic and sponsored search results
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Temporal click model for sponsored search
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Incorporating post-click behaviors into a click model
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Learning more powerful test statistics for click-based retrieval evaluation
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User browsing models: relevance versus examination
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Bayesian Browsing Model: Exact Inference of Document Relevance from Petabyte-Scale Data
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Learning click models via probit bayesian inference
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Explore click models for search ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Using related queries to improve web search results ranking
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Inferring search behaviors using partially observable markov model with duration (POMD)
Proceedings of the fourth ACM international conference on Web search and data mining
Click-graph modeling for facet attribute estimation of web search queries
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
A probability click tracking model analysis of web search results
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Evaluating search engines by clickthrough data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Click prediction for product search on C2C web sites
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Effects of Usage-Based Feedback on Video Retrieval: A Simulation-Based Study
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Characterizing search intent diversity into click models
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Evaluating new search engine configurations with pre-existing judgments and clicks
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Implicit association via crowd-sourced coselection
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Balancing exploration and exploitation in learning to rank online
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Smoothing click counts for aggregated vertical search
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The sum of its parts: reducing sparsity in click estimation with query segments
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Inferring and using location metadata to personalize web search
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Efficiently collecting relevance information from clickthroughs for web retrieval system evaluation
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Enhanced results for web search
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User-click modeling for understanding and predicting search-behavior
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Simulating simple user behavior for system effectiveness evaluation
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No search result left behind: branching behavior with browser tabs
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Incorporating revisiting behaviors into click models
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Personalized click model through collaborative filtering
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Beyond ten blue links: enabling user click modeling in federated web search
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Implicit: a multi-agent recommendation system for web search
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Survival analysis of click logs
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Can click patterns across user's query logs predict answers to definition questions?
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On caption bias in interleaving experiments
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Do ads compete or collaborate?: designing click models with full relationship incorporated
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Modeling browsing behavior for click analysis in sponsored search
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Evaluating implicit judgments from image search clickthrough data
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Model Based Comparison of Discounted Cumulative Gain and Average Precision
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A novel model for user clicks identification based on hidden semi-Markov
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Using intent information to model user behavior in diversified search
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Incorporating vertical results into search click models
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User model-based metrics for offline query suggestion evaluation
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Click model-based information retrieval metrics
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Fighting search engine amnesia: reranking repeated results
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Captions and biases in diagnostic search
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A vlHMM approach to context-aware search
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Click-boosting random walk for image search reranking
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Annotation for free: video tagging by mining user search behavior
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TellMyRelevance!: predicting the relevance of web search results from cursor interactions
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Incorporating user preferences into click models
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Modeling clicks beyond the first result page
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Mining search and browse logs for web search: A Survey
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Efficient Time-Stamped Event Sequence Anonymization
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Exploiting contextual factors for click modeling in sponsored search
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Estimating ad group performance in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
User modeling in search logs via a nonparametric bayesian approach
Proceedings of the 7th ACM international conference on Web search and data mining
From devices to people: attribution of search activity in multi-user settings
Proceedings of the 23rd international conference on World wide web
Entity ranking using click-log information
Intelligent Data Analysis
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Search engine click logs provide an invaluable source of relevance information but this information is biased because we ignore which documents from the result list the users have actually seen before and after they clicked. Otherwise, we could estimate document relevance by simple counting. In this paper, we propose a set of assumptions on user browsing behavior that allows the estimation of the probability that a document is seen, thereby providing an unbiased estimate of document relevance. To train, test and compare our model to the best alternatives described in the Literature, we gather a large set of real data and proceed to an extensive cross-validation experiment. Our solution outperforms very significantly all previous models. As a side effect, we gain insight into the browsing behavior of users and we can compare it to the conclusions of an eye-tracking experiments by Joachims et al. [12]. In particular, our findings confirm that a user almost always see the document directly after a clicked document. They also explain why documents situated just after a very relevant document are clicked more often.