@inproceedings{guo2009click,
author = {Guo, Fan and Liu, Chao and Kannan, Anitha and Minka, Tom and Taylor, Mike and Wang, Yi-Min and Faloutsos, Christos},
title = {Click Chain Model in Web Search},
booktitle = {WWW'09: Proceedings of the 18th International World Wide Web Conference},
year = {2009},
month = {April},
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 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.},
publisher = {Association for Computing Machinery, Inc.},
url = {http://approjects.co.za/?big=en-us/research/publication/click-chain-model-in-web-search/},
edition = {WWW'09: Proceedings of the 18th International World Wide Web Conference},
}