In this paper, we propose a solution that won the 10th prize in the KDD Cup 2023 Challenge Task 2 (Next Product Recommendation for Underrepresented Languages/Locales). Our approach involves two steps: (i) Identify candidate item sets based on co-visitation, and (ii) Re-ranking the items using LightGBM with locale-independent features, including session-based features and product similarity. The experiment demonstrated that the locale-independent model performed consistently well across different test locales, and performed even better when incorporating data from other locales into the training.

翻译：本文提出了一种在KDD Cup 2023挑战赛任务2（面向代表性不足语言/地区的下一产品推荐）中获得第十名的解决方案。我们的方法包含两个步骤：（i）基于共访关系识别候选商品集合，（ii）利用具有语言无关特征（包括会话特征和产品相似度）的LightGBM对商品进行重排序。实验表明，该语言无关模型在不同测试地区均表现出稳定性能，且在训练中引入其他地区数据时性能更优。