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
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
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
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
Spatio-temporal models for estimating click-through rate
Proceedings of the 18th international conference on World wide web
Hybrid keyword search auctions
Proceedings of the 18th international conference on World wide web
General auction mechanism for search advertising
Proceedings of the 18th international conference on World wide web
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
ACM Transactions on Knowledge Discovery from Data (TKDD)
Explore click models for search ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
ViewSer: enabling large-scale remote user studies of web search examination and interaction
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
User-click modeling for understanding and predicting search-behavior
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to advertise: how many ads are enough?
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
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
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
Relational click prediction for sponsored search
Proceedings of the fifth ACM international conference on Web search and data mining
The impact of images on user clicks in product search
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
Using boosted trees for click-through rate prediction for sponsored search
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
Multi-objective optimization for sponsored search
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
An Online Learning Framework for Refining Recency Search Results with User Click Feedback
ACM Transactions on Information Systems (TOIS)
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
Modeling browsing behavior for click analysis in sponsored search
Proceedings of the 21st ACM international conference on Information and knowledge management
Ad click prediction: a view from the trenches
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 22nd international conference on World Wide Web
Exploiting contextual factors for click modeling in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
Estimating ad group performance in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
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Recent advances in click model have positioned it as an attractive method for representing user preferences in web search and online advertising. Yet, most of the existing works focus on training the click model for individual queries, and cannot accurately model the tail queries due to the lack of training data. Simultaneously, most of the existing works consider the query, url and position, neglecting some other important attributes in click log data, such as the local time. Obviously, the click through rate is different between daytime and midnight. In this paper, we propose a novel click model based on Bayesian network, which is capable of modeling the tail queries because it builds the click model on attribute values, with those values being shared across queries. We called our work General Click Model (GCM) as we found that most of the existing works can be special cases of GCM by assigning different parameters. Experimental results on a large-scale commercial advertisement dataset show that GCM can significantly and consistently lead to better results as compared to the state-of-the-art works.