A Dynamic Bayesian Network Click Model for Web Search Ranking

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.

Speaker Details

Olivier Chapelle is a research scientist in the machine learning group of Yahoo! Research. He graduated in theoretical computer science from the Ecole Normale Supérieure de Lyon in 1999. From 1998 he has been working in AT&T Labs with V. Vapnik on Support Vector Machines and regularization theory. In 2002, he received a doctorate from the University of Paris 6 in the field of learning theory with advisors Vladimir Vapnik and Patrick Gallinari. He then pursued a post-doc at the Max Planck Institute in Tübingen. His current research interests include semi-supervised learning, kernel machines, structured output learning and ranking. He has published numerous journal papers, book chapters and has edited two books by the MIT Press.

Date:
Speakers:
Olivier Chapelle
Affiliation:
Yahoo! Research
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