A probability click tracking model analysis of web search results

Abstract

User click behaviors reflect his preference in Web search processing objectively, and it is very important to give a proper interpretation of user click for improving search results. Previous click models explore the relationship between user examines and latent clicks web document obtained by search result page via multiple-click model, such as the independent click model(ICM) or the dependent click model(DCM),which the examining-next probability only depends on the current click. However, user examination on a search result page is a continuous and relevant procedure. In this paper, we attempt to explore the historical clicked data using a probability click tracking model(PCTM). In our approach, the examine-next probability is decided by the click variables of each clicked result. We evaluate the proposed model on a real-world data set obtained from a commercial search engine. The experiment results illustrate that PCTM can achieve the competitive performance compared with the existing click models under standard metrics. © 2010 Springer-Verlag.