GraphMatch: Fusing Language and Graph Representations in a Dynamic Two-Sided Work Marketplace
Miko{\l}aj Sacha, Hammad Jafri, Mattie Terzolo, Ayan Sinha, Andrew Rabinovich

TL;DR
GraphMatch is a novel recommendation framework that combines pre-trained language models with graph neural networks to improve match predictions in dynamic, text-rich marketplaces, demonstrating superior performance and efficiency.
Contribution
It introduces a comprehensive method that fuses language and graph representations using adversarial sampling and subgraph training for real-time, large-scale recommendations.
Findings
Outperforms language-only and graph-only baselines.
Effective in capturing evolving text semantics and graph structure.
Suitable for low-latency, real-time inference.
Abstract
Recommending matches in a text-rich, dynamic two-sided marketplace presents unique challenges due to evolving content and interaction graphs. We introduce GraphMatch, a new large-scale recommendation framework that fuses pre-trained language models with graph neural networks to overcome these challenges. Unlike prior approaches centered on standalone models, GraphMatch is a comprehensive recipe built on powerful text encoders and GNNs working in tandem. It employs adversarial negative sampling alongside point-in-time subgraph training to learn representations that capture both the fine-grained semantics of evolving text and the time-sensitive structure of the graph. We evaluated extensively on interaction data from Upwork, a leading labor marketplace, at large scale, and discuss our approach towards low-latency inference suitable for real-time use. In our experiments, GraphMatch…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Topic Modeling · Multimodal Machine Learning Applications
