A Novel Click Model and Its Applications to Online Advertising

Recent advances in click model have positioned it as an attractive method for representing user preferences in web search and online advertising. Yet, most of the existing works focus on training the click model for individual queries, and cannot accurately model the tail queries due to the lack of training data. Simultaneously, most of the existing works consider the query, url and position, neglecting some other important attributes in click log data, such as the local time. Obviously, the click through rate is different between daytime and midnight. In this paper, we propose a novel click model based on Bayesian network, which is capable of modeling the tail queries because it builds the click model on attribute values, with those values being shared across queries. We called our work General Click Model (GCM) as we found that most of the existing works can be special cases of GCM by assigning different parameters. Experimental results on a largescale commercial advertisement datas...

• Type: Conference
• Year: 2010
• Where: WSDM
• Authors: Zeyuan Allen Zhu, Weizhu Chen, Tom Minka, Chenguang Zhu, Zheng Chen
• Keywords: Click Log Data, Click Model, Data Mining, General Click Model, WSDM 2010