Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
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
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning user interaction models for predicting web search result preferences
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
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
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
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
Modeling and predicting user behavior in sponsored search
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting bounce rates in sponsored search advertisements
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Inferring and using location metadata to personalize web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
No search result left behind: branching behavior with browser tabs
Proceedings of the fifth ACM international conference on Web search and data mining
A noise-aware click model for web search
Proceedings of the fifth ACM international conference on Web search and data mining
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
Modeling the impact of short- and long-term behavior on search personalization
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Improving searcher models using mouse cursor activity
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
On caption bias in interleaving experiments
Proceedings of the 21st ACM international conference on Information and knowledge management
Do ads compete or collaborate?: designing click models with full relationship incorporated
Proceedings of the 21st ACM international conference on Information and knowledge management
Evaluating implicit judgments from image search clickthrough data
Journal of the American Society for Information Science and Technology
Modeling click and relevance relationship for sponsored search
Proceedings of the 22nd international conference on World Wide Web companion
A click model for time-sensitive queries
Proceedings of the 22nd international conference on World Wide Web companion
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Much work has attempted to model a user's click-through behavior by mining the click logs. The task is not trivial due to the well-known position bias problem. Some break-throughs have been made: two newly proposed click models, DBN and CCM, addressed this problem and improved document relevance estimation. However, to further improve the estimation, we need a model that can capture more sophisticated user behaviors. In particular, after clicking a search result, a user's behavior (such as the dwell time on the clicked document, and whether there are further clicks on the clicked document) can be highly indicative of the relevance of the document. Unfortunately, such measures have not been incorporated in previous click models. In this paper, we introduce a novel click model, called the post-click click model (PCC), which provides an unbiased estimation of document relevance through leveraging both click behaviors on the search page and post-click behaviors beyond the search page. The PCC model is based on the Bayesian approach, and because of its incremental nature, it is highly scalable to large scale and constantly growing log data. Extensive experimental results illustrate that the proposed method significantly outperforms the state of the art methods merely relying on click logs.