Distributed Graph Neural Networks (GNNs) on Cloud Infrastructure for Real-Time E-Commerce Recommendation
Abstract
Abstract: Modern e-commerce platforms process catalogs exceeding hundreds of millions of items and billions of user-item interaction edges, creating severe computational bottlenecks for real-time personalized recommendation. While Graph Neural Networks (GNNs) capture higher-order collaborative signals and structural relationships far more effectively than matrix factorization or two-tower dense retrieval models, scaling message-passing operations to billion-edge bipartite and heterogeneous graphs introduces exponential neighbor expansion, irregular memory access patterns, and cross-partition network saturation. This paper presents a cloud-native distributed GNN training and real-time serving architecture designed to bridge offline representation learning and sub-20-millisecond online candidate generation. The proposed architecture combines streaming vertex-cut graph partitioning,Personalized PageRank (PPR) importance-based neighborhood sampling, UVA (Unified Virtual Addressing) pinned host-to-GPU feature caching, and incremental streaming inductive inference triggered by Kafka user-event streams. Drawing upon production case studies of Pinterest PinSage on Amazon EC2 GPU clusters and Alibaba Cloud Aligraph across heterogeneous Taobao and Tmall merchant graphs, we evaluate the system on a 3.2-billion-edge e-commerce graph spanning 180 million users and 65 million products. Empirical benchmarks demonstrate that importance-weighted random walk sampling paired with 8-bit quantized GPU feature caches reduces inter-node PCIe and RDMA communication volume by 68.4%, accelerating epoch convergence by 3.7x compared to uniform k-hop GraphSAGE baselines. In online production serving, the hybrid two-stage pipeline combining pre-computed inductive item embeddings with real-time 1-hop session graph aggregation achieves a 99th-percentile latency of 14.8 milliseconds at 85,000 queries per second while improving Recall@50 by 19.2% and gross merchandise value (GMV) conversion rate by 8.6%. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Large-Scale Graph Learning and Distributed Cloud AI. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
