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, existin...
| Authors | Ruizhong Qiu, Yinglong Xia, Dongqi Fu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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