ELGAR: Exposure-Ledger Guided Adaptive Routing for Fair Generative Recommendation

Generative recommendation has recently attracted growing attention, with Semantic-ID((SID)-based generative recommenders emerging as a major paradigm. They formulate recommendation as autoregressive item generation, where each item is represented by a sequence of discrete semantic tokens. Despite strong preference modeling, these models tend to over-expose popular items, leading to severe item-side unfairness. Existing methods face two limitations: misalignment, as improvements in tokenization or generation do not directly ensure alignment with the target item-exposure distribution; and rigidity, as offline corrections do not adapt to evolving exposure deficits across requests. We propose ELGAR, an Exposure-Ledger Guided Adaptive Routing framework for fair generative recommendation. ELGAR combines explicit group routing before semantic pruning with an Exposure Ledger that tracks cumulative exposure across requests. Together, they enable adaptive group-level allocation while preserving user relevance across different SID-based backbones. Experiments on four datasets and three backbones demonstrate improved fairness while maintaining recommendation performance. Our code is available at https://anonymous.4open.science/r/ELGAR-Anon-FB51.