Generative models offer a promising paradigm for the final stage reranking in multi-stage recommender systems, with the ability to capture inter-item dependencies within reranked lists. However, their practical deployment still faces two key challenges: (1) an inherent conflict between achieving high generation quality and ensuring low-latency inference, making it difficult to balance the two, and (2) insufficient interaction between user and item features in existing methods. To address these challenges, we propose a novel Personalized Semi-Autoregressive with online knowledge Distillation (PSAD) framework for reranking. In this framework, the teacher model adopts a semi-autoregressive generator to balance generation quality and efficiency, while its ranking knowledge is distilled online into a lightweight scoring network during joint training, enabling real-time and efficient inference. Furthermore, we propose a User Profile Network (UPN) that injects user intent and models interest dynamics, enabling deeper interactions between users and items. Extensive experiments conducted on three large-scale public datasets demonstrate that PSAD significantly outperforms state-of-the-art baselines in both ranking performance and inference efficiency.

翻译：生成模型为多阶段推荐系统中的最终重排序阶段提供了一种有前景的范式，其能够捕捉重排序列表内的物品间依赖关系。然而，其实际部署仍面临两个关键挑战：(1) 实现高生成质量与确保低延迟推理之间存在固有冲突，难以平衡二者；(2) 现有方法中用户与物品特征间的交互不足。为解决这些挑战，我们提出了一种新颖的个性化半自回归在线知识蒸馏（PSAD）重排序框架。在该框架中，教师模型采用半自回归生成器以平衡生成质量与效率，同时其排序知识在联合训练期间通过在线蒸馏传递至轻量级评分网络，从而实现实时高效的推理。此外，我们提出了用户画像网络（UPN），该网络注入用户意图并建模兴趣动态，实现了用户与物品间更深层次的交互。在三个大规模公开数据集上进行的广泛实验表明，PSAD在排序性能和推理效率两方面均显著优于现有最先进的基线方法。