Social relations are leveraged to tackle the sparsity issue of user-item interaction data in recommendation under the assumption of social homophily. However, social recommendation paradigms predominantly focus on homophily based on user preferences. While social information can enhance recommendations, its alignment with user preferences is not guaranteed, thereby posing the risk of introducing informational redundancy. We empirically discover that social graphs in real recommendation data exhibit low preference-aware homophily, which limits the effect of social recommendation models. To comprehensively extract preference-aware homophily information latent in the social graph, we propose Social Heterophily-alleviating Rewiring (SHaRe), a data-centric framework for enhancing existing graph-based social recommendation models. We adopt Graph Rewiring technique to capture and add highly homophilic social relations, and cut low homophilic (or heterophilic) relations. To better refine the user representations from reliable social relations, we integrate a contrastive learning method into the training of SHaRe, aiming to calibrate the user representations for enhancing the result of Graph Rewiring. Experiments on real-world datasets show that the proposed framework not only exhibits enhanced performances across varying homophily ratios but also improves the performance of existing state-of-the-art (SOTA) social recommendation models.

翻译：社交关系被用于缓解推荐系统中用户-物品交互数据的稀疏性问题，其前提是社交同质性假设。然而，社交推荐范式主要关注基于用户偏好的同质性。尽管社交信息可以增强推荐，但其与用户偏好的一致性无法保证，因此存在引入信息冗余的风险。我们通过实验发现，真实推荐数据中的社交图表现出较低偏好感知的同质性，这限制了社交推荐模型的效果。为了全面提取社交图中潜藏的偏好感知同质性信息，我们提出了一种数据为中心的框架——社交异质性缓解重连（SHaRe），用于增强现有基于图的社交推荐模型。我们采用图重连技术来捕获并添加高同质性的社交关系，同时切断低同质性（或异质性）关系。为了更好地从可靠的社交关系中优化用户表示，我们在SHaRe的训练中集成了对比学习方法，旨在校准用户表示以增强图重连的结果。在真实数据集上的实验表明，所提出的框架不仅在不同同质性比例下表现出增强的性能，还提升了现有最先进（SOTA）社交推荐模型的性能。