Graph-structured data is ubiquitous and powerful in representing complex relationships in many online platforms. While graph neural networks (GNNs) are widely used to learn from such data, counterfactual graph learning has emerged as a promising approach to improve model interpretability. Counterfactual explanation research focuses on identifying a counterfactual graph that is similar to the original but leads to different predictions. These explanations optimize two objectives simultaneously: the sparsity of changes in the counterfactual graph and the validity of its predictions. Building on these qualitative optimization goals, this paper introduces CFRecs, a novel framework that transforms counterfactual explanations into actionable insights. CFRecs employs a two-stage architecture consisting of a graph neural network (GNN) and a graph variational auto-encoder (Graph-VAE) to strategically propose minimal yet high-impact changes in graph structure and node attributes to drive desirable outcomes in recommender systems. We apply CFRecs to Zillow's graph-structured data to deliver actionable recommendations for both home buyers and sellers with the goal of helping them navigate the competitive housing market and achieve their homeownership goals. Experimental results on Zillow's user-listing interaction data demonstrate the effectiveness of CFRecs, which also provides a fresh perspective on recommendations using counterfactual reasoning in graphs.

翻译：图结构数据在众多在线平台中普遍存在，能够有效表征复杂关系。尽管图神经网络（GNN）被广泛用于从此类数据中学习，反事实图学习已成为提升模型可解释性的一种有前景的方法。反事实解释研究专注于识别一个与原始图相似但能导致不同预测的反事实图。这些解释同时优化两个目标：反事实图中改变的稀疏性及其预测的有效性。基于这些定性的优化目标，本文提出了CFRecs，一个将反事实解释转化为可操作见解的新型框架。CFRecs采用一个由图神经网络（GNN）和图变分自编码器（Graph-VAE）组成的两阶段架构，旨在策略性地提出对图结构和节点属性进行最小但高影响力的改变，以驱动推荐系统产生期望的结果。我们将CFRecs应用于Zillow的图结构数据，为购房者和售房者提供可操作的建议，旨在帮助他们应对竞争激烈的房地产市场并实现其置业目标。在Zillow用户-房源交互数据上的实验结果证明了CFRecs的有效性，该框架也为利用图中反事实推理进行推荐提供了一个新的视角。