GraphMatch: Fusing Language and Graph Representations in a Dynamic Two-Sided Work Marketplace
This provides an effective solution for text-rich, dynamic two-sided recommendations in platforms like labor marketplaces, though it appears incremental as a hybrid of existing methods.
The authors tackled the problem of recommending matches in text-rich, dynamic two-sided marketplaces by introducing GraphMatch, a framework that fuses pre-trained language models with graph neural networks. The result was that GraphMatch outperformed language-only and graph-only baselines on matching tasks while maintaining runtime efficiency.
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.