A Boring-yet-effective Approach for the Product Ranking Task of the Amazon KDD Cup 2022

arxiv(2022)

引用 0|浏览8
暂无评分
摘要
In this work we describe our submission to the product ranking task of the Amazon KDD Cup 2022. We rely on a receipt that showed to be effective in previous competitions: we focus our efforts towards efficiently training and deploying large language odels, such as mT5, while reducing to a minimum the number of task-specific adaptations. Despite the simplicity of our approach, our best model was less than 0.004 nDCG@20 below the top submission. As the top 20 teams achieved an nDCG@20 close to .90, we argue that we need more difficult e-Commerce evaluation datasets to discriminate retrieval methods.
更多
查看译文
关键词
product ranking task,amazon kdd cup,boring-yet-effective
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要