Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science and Technology
The comparative effectiveness of sponsored and nonsponsored links for Web e-commerce queries
ACM Transactions on the Web (TWEB)
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
Externalities in online advertising
Proceedings of the 17th international conference on World Wide Web
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
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
Characterizing commercial intent
Proceedings of the 18th ACM conference on Information and knowledge management
A novel click model and its applications to online advertising
Proceedings of the third 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
User-click modeling for understanding and predicting search-behavior
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting contextual factors for click modeling in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
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Clickthrough rate provides a fundamental measure of advertising quality, which is widely used in ad selection strategies. However, ads placed in contexts where they are rarely viewed, or where users are unlikely to be interested in commercial results, may receive few clicks regardless of their quality. In this paper, we gain insight into user browsing and click behavior for the purpose of click analysis in sponsored search domain. The list of ads displayed on a page, the user's initial motivation to browse this list, and the persistence of the user are among the contextual factors considered in this paper. We propose a probabilistic model for user's browsing and click behavior using these contextual factors. To evaluate the performance of the model, we compare it with state-of-the-art methods. The experimental results confirm that these contextual factors can better reflect user browsing and click behavior in sponsored search.