这篇论文提出了G2Rec框架，能更精准地建模用户兴趣，在工业推荐场景中效果优于现有方法。
G2Rec是一个面向工业级生成式推荐的可扩展框架，通过统一全局图协同建模与语义标记化，解决了现有方法难以同时注入用户行为与物品语义上下文的问题。在公共数据集和产品线在线部署实验中，G2Rec在推荐准确性上优于基于图序列化和图神经网络的基线方法。该框架无需用户真实兴趣标签即可生成语义化的用户兴趣原型，从而提升序贯推荐的上下文建模能力。
Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation
Generative recommendation is an emerging paradigm that has shown promise in industrial recommendation systems, aiming to predict users' next interactions from their historical behaviors. At the core of generative recommendation lies item tokenization, which bridges item semantics and recommendation models. However, existing methods often struggle to effectively organize and inject complex user-behavioral and item-semantic contexts into recommendation models simultaneously. On the one hand, existing graph-based integration methods, such as graph serialization and graph neural networks, either suffer from scalability issues or exploit only local graph information. On the other hand, existing semantic tokenization methods typically rely on heuristics and lack explicit supervision signals, which may lead to inaccurate or suboptimal semantic representations. To address these limitations in user interest context modeling, we propose G2Rec, a scalable framework that unifies holistic graph-based user co-engagement modeling with semantic tokenization for industrial-scale generative recommendation. Overall, G2Rec enables recommendation models to capture holistic and semantically grounded user interest prototypes without requiring ground-truth user interests, thereby providing more comprehensive and accurate modeling of user behavior contexts in industrial sequential recommendation. Online deployment across product surfaces and extensive experiments on public datasets demonstrate the superiority of G2Rec over existing methods.