User Click Detection in Ideal Sessions.

Users interact with search engine result pages in various ways, including their clicks, cursor movements, and page scrolls. Researchers model such user interaction behavior in order to understand users and improve search result presentation. In this paper we propose nine different user click models that take various real life search behavior into account. Our main assumption is that users base their clicks primarily on features of document titles, URLs, and snippets over the entire results page, as well as the queries submitted to the search engine and the documents seen previously in their session history. We evaluate our click models by their effectiveness at predicting actual user clicks using standard classification evaluation measures precision, recall, and area under the ROC curve (AUC). We show that incorporating information about the entire results page gives nearly 40% improvement in precision, and including information about the session history along with it increases precision by up to 43%.