Classifying and Characterizing Query Intent

Understanding the intent underlying users' queries may help personalize search results and improve user satisfaction. In this paper, we develop a methodology for using and clickthrough logs, query specific information, and the content of search engine result pages to study characterstics of query intents, specially commercial intents. 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.