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.

翻译：近年来，研究者利用社交关系来提升推荐性能。然而，大多数现有的社交推荐方法需要针对特定场景精心设计辅助社交任务，这高度依赖于领域知识和专业技能。为解决这一局限，我们提出面向社交推荐的自动自监督学习（AusRec），该方法将多个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的贡献。该设计使模型能够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征学习。在多个真实数据集上的大量实验表明，AusRec 持续优于最先进的基准方法，验证了其在不同推荐场景下的有效性和鲁棒性。