  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.
