Multi-stage Ensemble Model for Cross-market Recommendation

TL;DR

This paper presents a multi-stage ensemble model that combines data from different markets to improve cross-market recommendation accuracy, achieving a top-5 ranking in the WSDM Cup 2022 challenge.

Contribution

It introduces a novel multi-stage ensemble approach that integrates multiple recommenders and data sources for enhanced cross-market recommendation performance.

Findings

Ranked 4th in WSDM Cup 2022

Effective combination of recommenders improves accuracy

Multi-stage ensemble outperforms single models

Abstract

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.