Recommendations are treatments. While todays recommender systems attempt to
emulate the naturally occurring user behaviour by predicting either missing
entries in the user-item matrix or computing the most likely continuation of
user sessions, we need to start thinking of recommendations in terms of optimal
interventions with respect to specific goals, such as the increase of number of
user conversions on a E-Commerce website. This objective is known as
Incremental Treatment Effect prediction (ITE) in the causal community. We
propose a new way of factorizing user-item matrices created from a large sample
of biased data collected using a control recommendation policy and from limited
randomized recommendation data collected using a treatment recommendation
policy in order to jointly optimize the prediction of outcomes of the treatment
policy and its incremental treatment effect with respect to the control policy.
We compare our method against both state-of-the-art factorization methods and
against new approaches of causal recommendation and show significant
improvements in performance.