自动学习社交推荐中的辅助任务权重，提升模型表现。
Automatic Self-supervised Learning for Social Recommendations
- 通过元学习自动分配多个自监督任务的权重。
- 在多个真实数据集上超越现有最佳方法。
- 适合需要降低人工设计成本的推荐系统研究者。
近年来，研究人员利用社交关系提升推荐性能。然而，大多数现有社交推荐方法需要针对特定场景精心设计辅助社交任务，严重依赖领域知识和专业经验。为解决这一局限，我们提出自动自监督学习社交推荐（AusRec），将多种自监督辅助任务与自动加权机制相结合，通过元学习优化框架自适应平衡各任务贡献。该设计使模型能自动学习每个辅助任务的最优重要性，从而增强社交推荐中的表示学习。在多个真实世界数据集上的大量实验表明，AusRec持续优于当前最先进的基线方法，验证了其在不同推荐场景下的有效性和鲁棒性。
原文摘要 · Abstract (English)
In recent years, researchers have leveraged social relations to enhance recommendation performance. However, most existing social recommendation methods require carefully designed auxiliary social tasks tailored to specific scenarios, which depend heavily on domain knowledge and expertise. To address this limitation, we propose Automatic Self-supervised Learning for Social Recommendations (AusRec), which integrates multiple self-supervised auxiliary tasks with an automatic weighting mechanism to adaptively balance their contributions through a meta-learning optimization framework. This design enables the model to automatically learn the optimal importance of each auxiliary task, thereby enhancing representation learning in social recommendations. Extensive experiments on several real-world datasets demonstrate that AusRec consistently outperforms state-of-the-art baselines, validating its effectiveness and robustness across different recommendation scenarios.
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