论文标题
与混合预先排名和排名模型的跨市场推荐的有效方法
An Effective Way for Cross-Market Recommendation with Hybrid Pre-Ranking and Ranking Models
论文作者
论文摘要
WSDM CUP 2022的跨市场推荐任务是通过利用来自类似的高资源源市场的数据来找到解决方案来改善资源筛选目标市场中的个人推荐系统。最终，我们的球队OPDAI在排行榜上以NDCG@10分0.6773赢得了第一名。本文将详细介绍我们对此任务的解决方案。为了更好地将信息从来源市场转变为目标市场，我们采用了两个排名的阶段。在预级阶段，我们采用了多种预级方法或模型来发电。经过精心设计的功能分析和功能选择后，我们以10倍袋的方式训练LightGBM进行最终排名。
The Cross-Market Recommendation task of WSDM CUP 2022 is about finding solutions to improve individual recommendation systems in resource-scarce target markets by leveraging data from similar high-resource source markets. Finally, our team OPDAI won the first place with NDCG@10 score of 0.6773 on the leaderboard. Our solution to this task will be detailed in this paper. To better transform information from source markets to target markets, we adopt two stages of ranking. In pre-ranking stage, we adopt diverse pre-ranking methods or models to do feature generation. After elaborate feature analysis and feature selection, we train LightGBM with 10-fold bagging to do the final ranking.
