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A dynamic bayesian network click model for web search ranking

9 years 4 months ago
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. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]; H.3.5 [Online Information Services]; I.2.6 [ARTIFICIAL INTELLIGENCE]: Learning; I.6.m [SIMULATION AND MODELING]: Miscellaneous General Terms Algorithms, Design, Experimentation Keywords Cli...
Olivier Chapelle, Ya Zhang
Added 21 Nov 2009
Updated 21 Nov 2009
Type Conference
Year 2009
Where WWW
Authors Olivier Chapelle, Ya Zhang
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