English
资源说明：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 posi-
tion 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.
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