  For many internet businesses, presenting a given list of items in an order
that maximizes a certain metric of interest (e.g., click-through-rate, average
engagement time etc.) is crucial. We approach the aforementioned task from a
learning-to-rank perspective which reveals a new problem setup. In traditional
learning-to-rank literature, it is implicitly assumed that during the training
data generation one has access to the \emph{best or desired} order for the
given list of items. In this work, we consider a problem setup where we do not
observe the desired ranking. We present two novel solutions: the first solution
is an extension of already existing listwise learning-to-rank
technique--Listwise maximum likelihood estimation (ListMLE)--while the second
one is a generic machine learning based framework that tackles the problem in
its entire generality. We discuss several challenges associated with this
generic framework, and propose a simple \emph{item-payoff} and
\emph{positional-gain} model that addresses these challenges. We provide
training algorithms, inference procedures, and demonstrate the effectiveness of
the two approaches over traditional ListMLE on synthetic as well as on
real-life setting of ranking news articles for increased dwell time.
