Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models
Large Recommendation Models (LRMs) have demonstrated promising capabilities in industry-scale recommendation tasks. However, holistically integrating traditional signals into these transformer-based architectures effectively and efficiently remains a major challenge. Conventional approaches that "textualize" these signals directly or create discrete item representations often lead to excessively long prompts, substantial memory footprints, and high computational overhead. To overcome these limitations, we propose "Token Factory", a framework designed to transform traditional signals into "soft tokens" that can be directly processed by LRMs. This approach enables efficient integration and ...
| Authors | Xilun Chen, Shao-Chuan Wang, Baykal Cakici et al. |
|---|---|
| Primary category | cs.IR |
| Published | 17 June 2026 |
| Last updated at source | 27 July 2026 |
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