document: 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 .
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