TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors. An emerging alternative for discrete user representation -- using LLMs to generate text-based user tokens -- captures topical co-occurrences rather than deep sequential behavior dynamics and produces outputs that are difficult to ground to item attributes. Meanwhile, Semantic ID (SID) based item tokenization has proven effective for improving generalization in generative recommendation, yet discrete SID-based representations for users remain largely unexplored. We propose TokenMinds, an industrial-scale system that exten...
| Authors | Qingyun Liu, Bo Yan, Yang Liu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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