Distribution-Level Contrastive Supervision for Generative Recommendation
SODA, a plug-and-play alignment framework that adopts a BPR-style contrastive objective to align recommender representations with target-side distributional representations against negative ones, is developed and demonstrated that SODA consistently strengthens diverse generative recommendation architectures.