Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation

• URL: http://arxiv.org/abs/2603.07107v1
• Date: Sat, 07 Mar 2026 08:37:13 GMT
• Title: Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
• Abstract summary: We propose a novel Personalized Semi-Autoregressive with online knowledge Distillation (PSAD) framework for reranking.<n>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.<n>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.
• Score: 67.09494009022471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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