论文标题
跨市场推荐的多阶段合奏模型
Multi-stage Ensemble Model for Cross-market Recommendation
论文作者
论文摘要
本文介绍了我们团队在跨市场推荐中为2022年WSDM杯的解决方案。竞争的目的是有效利用从不同市场提取的信息，以提高两个目标市场建议的排名准确性。我们的模型由基于属于不同市场的数据组合的多阶段方法组成。在第一阶段，最先进的推荐人用于预测用户项目夫妇的分数，这些夫妇在接下来的两个阶段中都喜欢，采用简单的线性组合和更强大的梯度增强决策树技术。我们的团队在最终排行榜中排名第四。
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.
