Incorporating Post-Click Behaviors Into A Click Model

Much work has attempted to model a user's click-through behavior by mining the click logs. The task is not trivial due to the well-known position bias problem. Some breakthroughs have been made: two newly proposed click models, DBN and CCM, addressed this problem and improved document relevance estimation. However, to further improve the estimation, we need a model that can capture more sophisticated user behaviors. In particular, after clicking a search result, a user's behavior (such as the dwell time on the clicked document, and whether there are further clicks on the clicked document) can be highly indicative of the relevance of the document. Unfortunately, such measures have not been incorporated in previous click models. In this paper, we introduce a novel click model, called the post-click click model (PCC), which provides an unbiased estimation of document relevance through leveraging both click behaviors on the search page and post-click behaviors beyond the search page. The PCC model is based on the Bayesian approach, and because of its incremental nature, it is highly scalable to large scale and constantly growing log data. Extensive experimental results illustrate that the proposed method significantly outperforms the state of the art methods merely relying on click logs.