@inproceedings{www20092, booktitle = {18th International World Wide Web Conference}, month = {April}, title = {Click Chain Model in Web Search}, author = {Fan Guo and Chao Liu and Anitha Kannan and Tom Minka and Michael Taylor and Yi-Min Wang and Christos Faloutsos}, year = {2009}, pages = {11--11}, url = {http://www2009.eprints.org/2/}, abstract = {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 re?ect 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 ?rst and the last clicked position. } }