用图神经网络实现实时个性化推荐，提升电商购物体验。
Real-time and personalized product recommendations for large e-commerce platforms
- 结合图神经网络与简约学习，实现高效个性化推荐
- 在真实电商数据上准确预测用户购买序列，响应快速
- 适合大规模电商场景，尤其适合时尚类商品推荐
我们提出一种面向大型电商平台（特别是时尚零售）的实时个性化产品推荐方法。该方法旨在实现高精度、可扩展的推荐系统，同时保持极低的响应延迟，以保障用户体验。通过利用图神经网络和简约学习策略，模型在某全球最大电商平台的真实数据集上进行了广泛实验，有效预测用户购买序列，并能处理多交互场景。结果表明，在实际应用约束下，该方法仍能实现高效的个性化推荐，具备良好的实用性与可扩展性。
原文摘要 · Abstract (English)
We present a methodology to provide real-time and personalized product recommendations for large e-commerce platforms, specifically focusing on fashion retail. Our approach aims to achieve accurate and scalable recommendations with minimal response times, ensuring user satisfaction, leveraging Graph Neural Networks and parsimonious learning methodologies. Extensive experimentation with datasets from one of the largest e-commerce platforms demonstrates the effectiveness of our approach in forecasting purchase sequences and handling multi-interaction scenarios, achieving efficient personalized recommendations under real-world constraints.
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