Optimizing search by showing results in context
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improving personalized web search using result diversification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Rank-biased precision for measurement of retrieval effectiveness
ACM Transactions on Information Systems (TOIS)
Proceedings of the Second ACM International Conference on Web Search and Data Mining
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
Click chain model in web search
Proceedings of the 18th international conference on World wide web
Sources of evidence for vertical selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Vertical selection in the presence of unlabeled verticals
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A comparative analysis of cascade measures for novelty and diversity
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the fourth ACM international conference on Web search and data mining
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
A methodology for evaluating aggregated search results
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Evaluating diversified search results using per-intent graded relevance
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Learning to aggregate vertical results into web search results
Proceedings of the 20th ACM international conference on Information and knowledge management
Recency ranking by diversification of result set
Proceedings of the 20th ACM international conference on Information and knowledge management
Beyond ten blue links: enabling user click modeling in federated web search
Proceedings of the fifth ACM international conference on Web search and data mining
Click model-based information retrieval metrics
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Evaluating aggregated search using interleaving
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Modeling clicks beyond the first result page
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
ECIR 2013: 35th european conference on information retrieval
ACM SIGIR Forum
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A result page of a modern commercial search engine often contains documents of different types targeted to satisfy different user intents (news, blogs, multimedia). When evaluating system performance and making design decisions we need to better understand user behavior on such result pages. To address this problem various click models have previously been proposed. In this paper we focus on result pages containing fresh results and propose a way to model user intent distribution and bias due to different document presentation types. To the best of our knowledge this is the first work that successfully uses intent and layout information to improve existing click models.