用轻量网络实现高效个性化重排序，兼顾精度与速度。
Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
- 半自回归生成+在线知识蒸馏，平衡生成质量与推理速度。
- 在三个数据集上显著优于现有方法，性能与效率双提升。
- 适合需要实时推荐的工业场景，尤其关注用户兴趣动态。
生成式模型为多阶段推荐系统中的重排序阶段提供了新范式，能捕捉重排序列表内项目间的依赖关系。然而其实际部署仍面临两大挑战：(1) 高生成质量与低延迟推理之间存在固有矛盾，难以兼顾；(2) 现有方法中用户与项目特征交互不足。为此，我们提出一种新型个性化半自回归在线知识蒸馏（PSAD）框架。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，其排名知识通过在线蒸馏注入轻量评分网络，实现实时高效推理。此外，我们设计了用户画像网络（UPN），注入用户意图并建模兴趣动态，增强用户与项目的深层交互。在三个大规模公开数据集上的大量实验表明，PSAD 在排名性能与推理效率上均显著优于当前最优基线。
原文摘要 · Abstract (English)
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.
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