A completely locale-independent session-based recommender system by leveraging trained model

Yu Tokutake, Chihiro Yamasaki, Yongzhi Jin, Ayuka Inoue, Kei Harada

Keywords: recommender systems, session-based recommender systems, feature extraction, e-commerce

TL;DR: Our solution that won the 10th prize in Task 2 (Our code: https://gitlab.aicrowd.com/tktk/amazon-kddcup2023_dx2_solution)

Abstract: 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.

Submission Number: 6