RankGraph通过异构图学习，统一建模跨域推荐中的用户与物品关系。
RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation
- 构建多类型节点的异构图，动态提取用户-用户、物品-物品子图
- 在线测试中点击率提升0.92%，转化率提升2.82%
- 适合需要融合结构化关系知识的推荐系统研发
跨域推荐系统面临整合不同产品领域中细粒度用户与物品关系的挑战。为此，我们提出RankGraph，一个可扩展的图学习框架，作为推荐基础模型（FM）的核心组件。通过构建并利用包含多种产品实体的异构节点与边构成的图，RankGraph实现了用户、帖子、广告等实体间复杂关系的集成。该框架采用GPU加速的图神经网络与对比学习，支持动态提取如物品-物品、用户-用户等子图，用于相似性检索与实时聚类。此外，RankGraph将基于图的预训练表示作为上下文标记融入FM序列模型，增强其结构化关系知识。在真实线上A/B测试中，系统点击率提升0.92%，转化率提升2.82%，验证了其在跨域推荐场景下的有效性。
原文摘要 · Abstract (English)
Cross-domain recommendation systems face the challenge of integrating fine-grained user and item relationships across various product domains. To address this, we introduce RankGraph, a scalable graph learning framework designed to serve as a core component in recommendation foundation models (FMs). By constructing and leveraging graphs composed of heterogeneous nodes and edges across multiple products, RankGraph enables the integration of complex relationships between users, posts, ads, and other entities. Our framework employs a GPU-accelerated Graph Neural Network and contrastive learning, allowing for dynamic extraction of subgraphs such as item-item and user-user graphs to support similarity-based retrieval and real-time clustering. Furthermore, RankGraph integrates graph-based pretrained representations as contextual tokens into FM sequence models, enriching them with structured relational knowledge. RankGraph has demonstrated improvements in click (+0.92%) and conversion rates (+2.82%) in online A/B tests, showcasing its effectiveness in cross-domain recommendation scenarios.
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