Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation

Abstract

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. First, we construct a sparsified item-item co-engagement graph with \(\mathop {\operatorname{O}}(M\log M)\) edges as the item schema, where M denotes the total number of interactions. Second, we design a scalable “soft” clustering algorithm with time complexity \(\mathop {\operatorname{O}}(\rho M\log M)\) per iteration to extract distributed interest prototypes from the constructed graph, where ρ is a small constant representing the sparsity of the soft cluster membership distribution rather than a hard one-to-one assignment. Third, based on the interest prototypes and item interest profiles extracted by soft clustering, we tokenize these profiles together with users’ interested items to train a generative sequential recommendation model. 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. Proofs of theoretical results can be found at https://arxiv.org/abs/2606.20554.