Multi-stage Ensemble Model for Cross-market Recommendation
This work addresses the challenge of cross-market recommendation for competition participants, but it is incremental as it builds on existing methods without introducing new paradigms.
The paper tackled the problem of improving ranking accuracy in cross-market recommendation by combining data from different markets using a multi-stage ensemble approach, achieving a 4th place finish in the WSDM Cup 2022 competition.
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.