  Many current applications use recommendations in order to modify the natural
user behavior, such as to increase the number of sales or the time spent on a
website. This results in a gap between the final recommendation objective and
the classical setup where recommendation candidates are evaluated by their
coherence with past user behavior, by predicting either the missing entries in
the user-item matrix, or the most likely next event. To bridge this gap, we
optimize a recommendation policy for the task of increasing the desired outcome
versus the organic user behavior. We show this is equivalent to learning to
predict recommendation outcomes under a fully random recommendation policy. To
this end, we propose a new domain adaptation algorithm that learns from logged
data containing outcomes from a biased recommendation policy and predicts
recommendation outcomes according to random exposure. We compare our method
against state-of-the-art factorization methods, in addition to new approaches
of causal recommendation and show significant improvements.
