ACM SIGIR Forum
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic web query classification using labeled and unlabeled training data
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Detecting online commercial intention (OCI)
Proceedings of the 15th international conference on World Wide Web
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
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
Factors relating to the decision to click on a sponsored link
Decision Support Systems
An empirical analysis of sponsored search performance in search engine advertising
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning about the world through long-term query logs
ACM Transactions on the Web (TWEB)
Analysis of web search engine query session and clicked documents
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
The intention behind web queries
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
Characterizing commercial intent
Proceedings of the 18th ACM conference on Information and knowledge management
Estimating advertisability of tail queries for sponsored search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Understanding and predicting personal navigation
Proceedings of the fourth ACM international conference on Web search and data mining
The sum of its parts: reducing sparsity in click estimation with query segments
Information Retrieval
Post-click conversion modeling and analysis for non-guaranteed delivery display advertising
Proceedings of the fifth ACM international conference on Web search and data mining
Learning to predict the cost-per-click for your ad words
Proceedings of the 21st ACM international conference on Information and knowledge management
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Clickthrough rate, bid, and cost-per-click are known to be among the factors that impact the rank of an ad shown on a search result page. Search engines can benefit from estimating ad clickthrough in order to determine the quality of ads and maximize their revenue. In this paper, a methodology is developed to estimate ad clickthrough rate by exploring user queries and clickthrough logs. As we demonstrate, the average ad clickthrough rate depends to a substantial extent on the rank position of ads and on the total number of ads displayed on the page. This observation is utilized by a baseline model to calculate the expected clickthrough rate for various ads. We further study the impact of query intent on the clickthrough rate, where query intent is predicted using a combination of query features and the content of search engine result pages. The baseline model and the query intent model are compared for the purpose of calculating the expected ad clickthrough rate. Our findings suggest that such factors as the rank of an ad, the number of ads displayed on the result page, and query intent are effective in estimating ad clickthrough rate.