Modern search engines provide the result including not only the traditional web documents, but also the segments aggregated with specialized information such as images, videos, and news, called vertical, to the users. Most click models associated with...
Modern search engines provide the result including not only the traditional web documents, but also the segments aggregated with specialized information such as images, videos, and news, called vertical, to the users. Most click models associated with vertical search were designed to reduce various biases caused by verticals in search engine result pages to evaluate user behavior of web documents. However, evaluation of verticals themselves has not been conducted. In this paper, we propose vertical click models to analyze user behavior associated with both verticals and documents. The proposed click models based on probabilistic graphical model framework directly reflect events occurred in verticals and documents, so that they allow to elaborately calculate and evaluate the preference of verticals felt by the user. The proposed models were evaluated with a click log dataset from the popular commercial search engine in Korea. The results demonstrated superiority of our model over existing models in terms of normalized discounted cumulative gain.