  Learning-to-rank (LTR) algorithms are ubiquitous and necessary to explore the
extensive catalogs of media providers. To avoid the user examining all the
results, its preferences are used to provide a subset of relatively small size.
The user preferences can be inferred from the interactions with the presented
content if explicit ratings are unavailable. However, directly using implicit
feedback can lead to learning wrong relevance models and is known as biased
LTR. The mismatch between implicit feedback and true relevances is due to
various nuisances, with position bias one of the most relevant. Position bias
models consider that the lack of interaction with a presented item is not only
attributed to the item being irrelevant but because the item was not examined.
This paper introduces a method for modeling the probability of an item being
seen in different contexts, e.g., for different users, with a single estimator.
Our suggested method, denoted as contextual (EM)-based regression, is
ranker-agnostic and able to correctly learn the latent examination
probabilities while only using implicit feedback. Our empirical results
indicate that the method introduced in this paper outperforms other existing
position bias estimators in terms of relative error when the examination
probability varies across queries. Moreover, the estimated values provide a
ranking performance boost when used to debias the implicit ranking data even if
there is no context dependency on the examination probabilities.
