RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation

• URL: http://arxiv.org/abs/2509.02942v1
• Date: Wed, 03 Sep 2025 02:16:50 GMT
• Title: RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation
• Authors: Renzhi Wu, Junjie Yang, Li Chen, Hong Li, Li Yu, Hong Yan,
• Abstract summary: RankGraph is a scalable graph learning framework designed to serve as a core component in recommendation foundation models (FMs)<n>Our framework employs a GPU-accelerated Graph Neural Network and contrastive learning, allowing for dynamic extraction of subgraphs.<n>RankGraph has demonstrated improvements in click (+0.92%) and conversion rates (+2.82%) in online A/B tests.
• Score: 26.878738892518427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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