Towards Practical Large-scale Dynamical Heterogeneous Graph Embedding: Cold-start Resilient Recommendation

2025-12-15

AI Summary

To address scalability, data freshness, and cold-start challenges in industrial deployment of large-scale dynamic heterogeneous graph embedding, this paper proposes a two-stage framework integrating static global modeling with real-time incremental updates. The method introduces HetSGFormer—a linearly scalable heterogeneous graph Transformer for global structural modeling—and ILLE, a lightweight incremental local linear embedding algorithm enabling millisecond-level local updates on billion-node graphs. Further, CPU-native incremental optimization and heterogeneous graph structure encoding are incorporated to enhance cold-start robustness. A/B testing on a billion-scale industrial graph demonstrates that HetSGFormer increases advertiser value by 6.11%; ILLE delivers an additional 3.22% gain; and embedding refresh latency is reduced by 83.2%, significantly improving temporal responsiveness.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Deploying dynamic heterogeneous graph embeddings in production faces key challenges of scalability, data freshness, and cold-start. This paper introduces a practical, two-stage solution that balances deep graph representation with low-latency incremental updates. Our framework combines HetSGFormer, a scalable graph transformer for static learning, with Incremental Locally Linear Embedding (ILLE), a lightweight, CPU-based algorithm for real-time updates. HetSGFormer captures global structure with linear scalability, while ILLE provides rapid, targeted updates to incorporate new data, thus avoiding costly full retraining. This dual approach is cold-start resilient, leveraging the graph to create meaningful embeddings from sparse data. On billion-scale graphs, A/B tests show HetSGFormer achieved up to a 6.11% lift in Advertiser Value over previous methods, while the ILLE module added another 3.22% lift and improved embedding refresh timeliness by 83.2%. Our work provides a validated framework for deploying dynamic graph learning in production environments.

Problem

• Develops cold-start resilient recommendation using dynamic heterogeneous graph embeddings
• Addresses scalability and data freshness challenges in production graph learning systems
• Balances deep graph representation with low-latency incremental updates for billion-scale graphs

Innovation

• Two-stage framework balances deep representation with incremental updates
• HetSGFormer transformer provides linear scalability for global structure
• ILLE algorithm enables real-time, CPU-based updates without full retraining