Towards Unbiased Generative Recommendation

Generative recommendation (GenRec) retrieves items by generating identifiers from user histories; semantic ID (SID)-based systems represent each item as a sequence of tokens. Imbalanced interactions repeatedly reinforce the token paths of popular items, potentially weakening the influence of user history. Balanced SID assignments need not balance interaction frequencies, while global token counts can hide imbalances among the valid continuations of a particular prefix. We propose UniGenRec, which explicitly factors historical frequency at each SID prefix into the training distribution alongside the history-conditioned generator. A gate conditioned on user history and SID prefix controls this factor during supervised fine-tuning; we extend the same factorization to policy optimization. At inference, the frequency branch and gate are removed; only the generator is used. Across three datasets, three SID-based GenRec configurations (TIGER, MiniOneRec, and LC-Rec), and three generative backbones, UniGenRec improves recommendation performance over the corresponding base models and leads the compared methods on MIND-small, KuaiRec-small, and Beauty. Recommendation performance is 3.38× the base model on MIND-small, 2.37× on KuaiRec-small, and 1.51× on Beauty. It also broadens catalog coverage and reduces head-item amplification relative to training clicks.