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.

翻译：本文介绍了我们的PolimiRank团队对2022年WSDM杯跨市场建议的解决方案。竞争的目的是有效利用从不同市场获取的信息,提高两个目标市场的建议的排名准确性。我们的模式是采用基于不同市场数据组合的多阶段方法。在第一阶段,最先进的推荐人用来预测用户-项目夫妇的分数,这些夫妇在接下来的2个阶段被组合在一起,使用简单的线性组合和更强大的梯度推动决定树技术。我们的团队在最后的领头板中排名第4位。