GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation
Generative Retrieval (GR) offers a promising paradigm for recommendation through next-token prediction (NTP). However, scaling it to large-scale industrial systems introduces three challenges: (i) within a single request, the identical model inputs may produce inconsistent outputs due to the pagination request mechanism; (ii) the prohibitive cost of encoding long user behavior sequences with multi-token item representations based on semantic IDs, and (iii) aligning the generative policy with nuanced user preference signals. We present GenRec, a preference-oriented generative framework deployed on the JD App that addresses above challenges within a single decoder-only architecture. For tra...
| Authors | Yanyan Zou, Junbo Qi, Lunsong Huang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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