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

TL;DR

This paper introduces a scalable, real-time personalized recommendation system for large e-commerce platforms using Graph Neural Networks, demonstrating effectiveness in fashion retail with minimal response times.

Contribution

It presents a novel methodology combining Graph Neural Networks and parsimonious learning for real-time, scalable, and accurate personalized recommendations in large e-commerce settings.

Findings

Effective purchase sequence forecasting

Handles multi-interaction scenarios efficiently

Achieves minimal response times in real-world tests

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.