ACM SIGIR Forum
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Detecting online commercial intention (OCI)
Proceedings of the 15th international conference on World Wide Web
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
Understanding and predicting personal navigation
Proceedings of the fourth ACM international conference on Web search and data mining
Scalable multi-dimensional user intent identification using tree structured distributions
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Language intent models for inferring user browsing behavior
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Sponsored search auctions: an overview of research with emphasis on game theoretic aspects
Electronic Commerce Research
Interactive exploratory search for multi page search results
Proceedings of the 22nd international conference on World Wide Web
"Piaf" vs "Adele": classifying encyclopedic queries using automatically labeled training data
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Efficient parsing-based search over structured data
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Lessons from the journey: a query log analysis of within-session learning
Proceedings of the 7th ACM international conference on Web search and data mining
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Understanding the intent underlying users' queries may help personalize search results and improve user satisfaction. In this paper, we develop a methodology for using ad clickthrough logs, query specific information, and the content of search engine result pages to study characteristics of query intents, specially commercial intent. The findings of our study suggest that ad clickthrough features, query features, and the content of search engine result pages are together effective in detecting query intent. We also study the effect of query type and the number of displayed ads on the average clickthrough rate. As a practical application of our work, we show that modeling query intent can improve the accuracy of predicting ad clickthrough for previously unseen queries.