CFRecs: 基于房地产用户列表交互图的反事实推荐
CFRecs: Counterfactual Recommendations on Real Estate User Listing Interaction Graphs
AI总结：
CFRecs通过反事实推理在房地产用户列表交互图中生成可操作的推荐，提升推荐系统的可解释性和实用性。
摘要：
图结构数据在许多在线平台中普遍存在，并且在表示复杂关系方面非常强大。虽然图神经网络（GNNs）被广泛用于从此类数据中学习，但反事实图学习作为一种改进模型可解释性的方法已经涌现出来。反事实解释研究专注于识别一个与原始图相似但导致不同预测的反事实图。这些解释同时优化两个目标：反事实图中变化的稀疏性和其预测的有效性。基于这些定性优化目标，本文引入了CFRecs，一种新颖的框架，将反事实解释转化为可操作的见解。CFRecs采用由图神经网络（GNN）和图变分自编码器（Graph-VAE）组成的两阶段架构，以战略性地提出最小但影响较大的图结构和节点属性变化，以驱动推荐系统中的理想结果。我们将在Zillow的图结构数据上应用CFRecs，为房主和卖方提供可操作的推荐，旨在帮助他们应对竞争激烈的房地产市场并实现购房目标。在Zillow用户-列表交互数据上的实验结果证明了CFRecs的有效性，同时也为使用图中的反事实推理进行推荐提供了新的视角。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
