title: Click Chain Model in Web Search creator: Guo, Fan creator: Liu, Chao creator: Kannan, Anitha creator: Minka, Tom creator: Taylor, Michael creator: Wang, Yi-Min creator: Faloutsos, Christos description: 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, over 6.2% better click perplexity and much more robust (up to 30%) prediction of the first and the last clicked position. date: 2009-04 type: Conference or Workshop Item type: PeerReviewed format: application/pdf identifier: http://www2009.eprints.org/2/1/p11.pdf format: application/vnd.ms-powerpoint identifier: http://www2009.eprints.org/2/2/www09_ccm.ppt identifier: Guo, Fan and Liu, Chao and Kannan, Anitha and Minka, Tom and Taylor, Michael and Wang, Yi-Min and Faloutsos, Christos (2009) Click Chain Model in Web Search. In: 18th International World Wide Web Conference, April 20th-24th, 2009, Madrid, Spain. relation: http://www2009.eprints.org/2/