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：社会关系被用于缓解推荐中用户-物品交互数据的稀疏性问题，其前提是社会同质性假设。然而，社会推荐范式主要关注基于用户偏好的同质性。尽管社会信息可以增强推荐效果，但其与用户偏好的对齐并不得到保证，从而存在引入信息冗余的风险。我们通过实证发现，真实推荐数据中的社会图表现出较低的偏好感知同质性，这限制了社会推荐模型的效果。为了全面提取社会图中潜在的偏好感知同质性信息，我们提出Social Heterophily-alleviating Rewiring (SHaRe)，一种用于增强现有基于图的社会推荐模型的数据中心框架。我们采用图重连技术来捕获并添加高同质性的社会关系，同时剪除低同质性（或异质性）的关系。为了从可靠的社会关系中更好地精炼用户表示，我们在SHaRe训练中整合了一种对比学习方法，旨在校准用户表示以增强图重连的结果。在真实世界数据集上的实验表明，所提出的框架不仅在改变同质性比率时表现出增强的性能，还提升了现有最先进（SOTA）社会推荐模型的性能。