Temporal click model for sponsored search

Previous studies on search engine click modeling have identified two presentation factors that affect users' behavior: (1) position bias: the same result will get a different number of clicks when displayed in different positions and (2) externalities: the same result might get more clicks when displayed with results of relatively lower quality than when shown with higher quality results. In this paper we focus on analyzing the sequence of user actions to model users' click behavior on sponsored listings shown on the search results page. We first show that temporal click sequences are good indicators of externalities in the advertising domain. We then describe the positional rationality hypothesis to explain both the position bias and the externalities, and based on this hypothesis we further propose the temporal click model (TCM), a Bayesian framework that is scalable and computationally efficient. To the best of our knowledge, this is the first attempt in the literature to estimate positional bias, externalities and unbiased user-perceived ad quality from user click logs in a combined model. We finally evaluate the proposed model on two real datasets, each containing over 100 million ad impressions obtained from a commercial search engine. The experimental results show that TCM outperforms two other competitive methods at click prediction.