Click chain model in web search

Given a terabyte click log, can we build an efficient and effective click model? It is commonly believed that web search click logs are a gold mine for search business, because they reflect users' preference over web documents presented by the search engine. Click models provide a principled approach to inferring user-perceived relevance of web documents, which can be leveraged in numerous applications in search businesses. Due to the huge volume of click data, scalability is a must. We present the click chain model (CCM), which is based on a solid, Bayesian framework. It is both scalable and incremental, perfectly meeting the computational challenges imposed by the voluminous click logs that constantly grow. We conduct an extensive experimental study on a data set containing 8.8 million query sessions obtained in July 2008 from a commercial search engine. CCM consistently outperforms two state-of-the-art competitors in a number of metrics, with over 9.7% better log-likelihood, o...

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Anitha Kannan, Chao Liu 0001, Christos Faloutsos, Fan Guo, Michael J. Taylor, Tom Minka, Yi Min Wang
• Keywords: Better Click Perplexity, Internet Technology, Search Click Logs, Voluminous Click Logs, WWW 2009