creators_name: Chapelle, Olivier
creators_name: Zhang, Ya
type: conference_item
datestamp: 2009-04-06 19:08:32
lastmod: 2009-04-22 10:17:35
metadata_visibility: show
title: A Dynamic Bayesian Network Click Model for Web Search Ranking
ispublished: pub
full_text_status: public
pres_type: paper
abstract: As with any application of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can also provide an important source of implicit feedback and can be used as a cheap proxy for editorial labels. The main difficulty however comes from the so called position bias — urls appearing in lower positions are less likely to be clicked even if they are relevant. In this paper, we propose a Dynamic Bayesian Network which aims at providing us with unbiased estimation of the relevance from the click logs. Experiments show that the proposed click model outperforms other existing click models in predicting both click-through rate and relevance.
date: 2009-04
pagerange: 1-1
event_title: 18th International World Wide Web Conference
event_location: Madrid, Spain
event_dates: April 20th-24th, 2009
event_type: conference
refereed: TRUE
citation: Chapelle, Olivier <http://www2009.eprints.org/view/author/Chapelle=3AOlivier=3A=3A.html> and Zhang, Ya <http://www2009.eprints.org/view/author/Zhang=3AYa=3A=3A.html> (2009) A Dynamic Bayesian Network Click Model for Web Search Ranking. In: 18th International World Wide Web Conference, April 20th-24th, 2009, Madrid, Spain.
document_url: http://www2009.eprints.org/1/1/p1.pdf
document_url: http://www2009.eprints.org/1/2/clicks_chapelle.pdf