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In this paper, we propose a solution that won the 10th prize in the KDD Cup2023 Challenge Task 2 (Next Product Recommendation for UnderrepresentedLanguages/Locales). Our approach involves two steps: (i) Identify candidateitem sets based on co-visitation, and (ii) Re-ranking the items using LightGBMwith locale-independent features, including session-based features and productsimilarity. The experiment demonstrated that the locale-independent modelperformed consistently well across different test locales, and performed evenbetter when incorporating data from other locales into the training.

A Completely Locale-independent Session-based Recommender System by Leveraging Trained Model