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. I will present the click chain model, 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.

Joint work with Chao Liu, Anitha Kannan, Tom Minka, Michael Taylor, Yi-Min Wang and Christos Faloutsos.

Venue, Date, and Time

Venue: Wean Hall 4623

Date: Monday, May 4, 2009

Time: 12:00 noon