Real-time and personalized product recommendations for large e-commerce platforms

2025-06-26

AI Summary

Addressing the challenge of simultaneously achieving low latency, high accuracy, and scalability in real-time personalized recommendation for large-scale fashion e-commerce platforms, this paper proposes a novel recommendation framework integrating Graph Neural Networks (GNNs) with parsimonious learning. The method jointly models users’ heterogeneous interaction behaviors and dynamic purchase sequences, augmented by real-time streaming feature engineering to enable millisecond-level inference. Its key innovation lies in the first synergistic adoption of GNNs and parsimonious learning in recommendation systems—significantly reducing model complexity without compromising predictive accuracy. Evaluated on a large-scale industrial e-commerce dataset, the framework achieves a 12.7% improvement in Recall@20 over state-of-the-art baselines, maintains a P99 latency under 80 ms, and sustains online serving throughput of up to ten million users per second—effectively balancing accuracy, efficiency, and scalability.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Real-time personalized recommendations for large e-commerce platforms
• Accurate scalable fashion recommendations using Graph Neural Networks
• Forecasting purchase sequences with minimal response times

Innovation

• Graph Neural Networks for recommendations
• Parsimonious learning methodologies
• Real-time personalized purchase forecasting