融合语言与图结构，提升动态人才匹配效率
GraphMatch: Fusing Language and Graph Representations in a Dynamic Two-Sided Work Marketplace
- 双路协同：预训练语言模型与图神经网络联合建模
- 在Upwork数据上显著优于纯文本或纯图基线
- 支持低延迟推理，适合实时推荐场景
在文本丰富、动态变化的双边市场中进行匹配推荐面临独特挑战，因内容与交互图谱持续演化。我们提出GraphMatch，一种大规模推荐框架，将预训练语言模型与图神经网络融合，以应对这些挑战。不同于以往依赖单一模型的方法，GraphMatch是一个完整方案，结合强大文本编码器与图神经网络协同工作。其采用对抗性负采样与时间快照子图训练，学习能捕捉文本细粒度语义及图结构时序特性的表示。我们在领头劳动力市场平台Upwork上进行了大规模评估，并探讨了适用于实时场景的低延迟推理策略。实验表明，GraphMatch在匹配任务上超越纯语言模型与纯图模型基线，同时运行高效。结果证明，统一语言与图表示能有效解决文本丰富的动态双边推荐问题，实践上弥合了强大预训练语言模型与大规模图谱之间的鸿沟。
原文摘要 · Abstract (English)
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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