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

TL;DR

This paper introduces a locale-independent session-based recommender system that leverages trained models and LightGBM to improve product recommendations across diverse locales, demonstrating consistent performance and benefits from multi-locale data integration.

Contribution

The paper presents a novel two-step approach combining co-visitation candidate selection with locale-independent re-ranking using LightGBM, enhancing cross-locale recommendation effectiveness.

Findings

Locale-independent model performs consistently across different locales.

Incorporating multi-locale data improves recommendation accuracy.

The approach won 10th place in the KDD Cup 2023 Challenge.

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.