Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918378147414016 |
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| author | Cheng, Kai Wang, Hao Guo, Wei Liu, Weiwen Liu, Yong Li, Yawen Chen, Enhong |
| author_facet | Cheng, Kai Wang, Hao Guo, Wei Liu, Weiwen Liu, Yong Li, Yawen Chen, Enhong |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_07107 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation Cheng, Kai Wang, Hao Guo, Wei Liu, Weiwen Liu, Yong Li, Yawen Chen, Enhong Information Retrieval Artificial Intelligence 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. |
| title | Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2603.07107 |