Predictive user click models based on click-through history

Web search engines consistently collect information about users interaction with the system: they record the query they issued, the URL of presented and selected documents along with their ranking. This information is very valuable: It is a poll over millions of users on the most various topics and it has been used in many ways to mine users interests and preferences. Query logs have the potential to partially alleviate the search engines from thousand of searches by providing a way to predict answers for a subset of queries and users without knowing the content of a document. Even if the predicted result is at rank one, this analysis might be of interest: If there is enough condence on a user's click, we might redirect the user directly to the page whose link would be clicked. In this paper, we present three dierent models for predicting user clicks, ranging from most specic ones (using only past user history for the query) to very general ones (aggregating data over all user...

• Type: Conference
• Year: 2007
• Where: CIKM
• Authors: Benjamin Piwowarski, Hugo Zaragoza
• Keywords: CIKM 2007, Dierent Models, Query Log, Search Engines