用自蒸馏和生成式自监督学习提升图Transformer推荐效果
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems
- 结合生成式自监督与残差图Transformer，自动挖掘用户-物品交互逻辑
- 在多个数据集上超越基线，显著提升推荐性能
- 适合关注自监督学习与推荐系统融合的研究者
本文提出一种增强推荐系统的前沿方法，将生成式自监督学习（SSL）与残差图Transformer相结合。该方法通过有理据感知的自监督预训练任务实现高质量数据增强，自动提炼用户与物品间的交互规律。残差图Transformer引入拓扑感知的Transformer以捕捉全局上下文，并通过残差连接优化图表示学习。此外，自蒸馏过程对自监督信号进行迭代优化，挖掘一致的协同推理模式。在多个数据集上的实验表明，该方法持续优于基准模型。
原文摘要 · Abstract (English)
This paper introduces a cutting-edge method for enhancing recommender systems through the integration of generative self-supervised learning (SSL) with a Residual Graph Transformer. Our approach emphasizes the importance of superior data enhancement through the use of pertinent pretext tasks, automated through rationale-aware SSL to distill clear ways of how users and items interact. The Residual Graph Transformer incorporates a topology-aware transformer for global context and employs residual connections to improve graph representation learning. Additionally, an auto-distillation process refines self-supervised signals to uncover consistent collaborative rationales. Experimental evaluations on multiple datasets demonstrate that our approach consistently outperforms baseline methods.
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