Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
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 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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
User-click modeling for understanding and predicting search-behavior
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Personalized click model through collaborative filtering
Proceedings of the fifth ACM international conference on Web search and data mining
Improving searcher models using mouse cursor activity
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Model Based Comparison of Discounted Cumulative Gain and Average Precision
Journal of Discrete Algorithms
Captions and biases in diagnostic search
ACM Transactions on the Web (TWEB)
Proceedings of the 22nd international conference on World Wide Web
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Recent advances in click models have positioned them as an effective approach to the improvement of interpreting click data, and some typical works include UBM, DBN, CCM, etc. After formulating the knowledge of user search behavior into a set of model assumptions, each click model developed an inference method to estimate its parameters. The inference method plays a critical role in terms of accuracy in interpreting clicks, and we observe that different inference methods for a click model can lead to significant accuracy differences. In this paper, we propose a novel Bayesian inference approach for click models. This approach regards click model under a unified framework, which has the following characteristics and advantages: 1. This approach can be widely applied to existing click models, and we demonstrate how to infer DBN, CCM and UBM through it. This novel inference method is based on the Bayesian framework which is more flexible in characterizing the uncertainty in clicks and brings higher generalization abilities. As a result, it not only excels in the inference methods originally developed in click models, but also provides a valid comparison among different models; 2. In contrast to the previous click models, which are exclusively designed for the position-bias, this approach is capable of capturing more sophisticated information such as BM25 and PageRank score into click models. This makes these models interpret click-through data more accurately. Experimental results illustrate that the click models integrated with more information can achieve significantly better performance on click perplexity and search ranking; 3. Because of the incremental nature of the Bayesian learning, this approach is scalable to process large scale and constantly growing log data.