  This tutorial covers and contrasts the two main methodologies in unbiased
Learning to Rank (LTR): Counterfactual LTR and Online LTR. There has long been
an interest in LTR from user interactions, however, this form of implicit
feedback is very biased. In recent years, unbiased LTR methods have been
introduced to remove the effect of different types of bias caused by
user-behavior in search. For instance, a well addressed type of bias is
position bias: the rank at which a document is displayed heavily affects the
interactions it receives. Counterfactual LTR methods deal with such types of
bias by learning from historical interactions while correcting for the effect
of the explicitly modelled biases. Online LTR does not use an explicit user
model, in contrast, it learns through an interactive process where randomized
results are displayed to the user. Through randomization the effect of
different types of bias can be removed from the learning process. Though both
methodologies lead to unbiased LTR, their approaches differ considerably,
furthermore, so do their theoretical guarantees, empirical results, effects on
the user experience during learning, and applicability. Consequently, for
practitioners the choice between the two is very substantial. By providing an
overview of both approaches and contrasting them, we aim to provide an
essential guide to unbiased LTR so as to aid in understanding and choosing
between methodologies.
