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arXiv · 2512.02849

GraphMatch: Fusing Language and Graph Representations in a Dynamic Two-Sided Work Marketplace

Mikołaj Sacha·Hammad Jafri·Mattie Terzolo·Ayan Sinha·Andrew Rabinovich
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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 outperforms language-only and graph-only baselines on matching tasks while being efficient at runtime. These results demonstrate that unifying language and graph representations yields a highly effective solution to text-rich, dynamic two-sided recommendations, bridging the gap between powerful pretrained LMs and large-scale graphs in practice.

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Mikołaj Sacha, Hammad Jafri, Mattie Terzolo, Ayan Sinha, Andrew Rabinovich. 2025-12-02. GraphMatch: Fusing Language and Graph Representations in a Dynamic Two-Sided Work Marketplace. https://arxiv.org/abs/2512.02849

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