  This paper describes the solution of our team PolimiRank for the WSDM Cup
2022 on cross-market recommendation. The goal of the competition is to
effectively exploit the information extracted from different markets to improve
the ranking accuracy of recommendations on two target markets. Our model
consists in a multi-stage approach based on the combination of data belonging
to different markets. In the first stage, state-of-the-art recommenders are
used to predict scores for user-item couples, which are ensembled in the
following 2 stages, employing a simple linear combination and more powerful
Gradient Boosting Decision Tree techniques. Our team ranked 4th in the final
leaderboard.
