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.
@article{arxiv.2310.07281,
title = {A Completely Locale-independent Session-based Recommender System by Leveraging Trained Model},
author = {Yu Tokutake and Chihiro Yamasaki and Yongzhi Jin and Ayuka Inoue and Kei Harada},
journal= {arXiv preprint arXiv:2310.07281},
year = {2023}
}