Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations

2025-02-12

arXiv.org

AI Summary

Existing GNN explanation methods focus primarily on node- or graph-level tasks and lack dedicated support for explainability in heterogeneous link prediction (LP), hindering trustworthiness and user engagement in recommendation systems. Method: This paper introduces the first GNN explanation framework tailored for heterogeneous LP, specifically designed for real-estate recommendation. It integrates structural and attribute perturbation with domain-knowledge-guided search space pruning to ensure both explanation fidelity and relevance while improving computational efficiency. The framework incorporates ZiGNN—a heterogeneous GNN—and leverages real-world Zillow heterogeneous graph data to generate context-aware, natural-language-level attribution explanations. Contribution/Results: Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches in both explanation quality (e.g., faithfulness, plausibility) and computational efficiency, effectively enhancing user trust and interaction intent in practical recommendation scenarios.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Transparency and interpretability are crucial for enhancing customer confidence and user engagement, especially when dealing with black-box Machine Learning (ML)-based recommendation systems. Modern recommendation systems leverage Graph Neural Network (GNN) due to their ability to produce high-quality recommendations in terms of both relevance and diversity. Therefore, the explainability of GNN is especially important for Link Prediction (LP) tasks since recommending relevant items can be viewed as predicting links between users and items. GNN explainability has been a well-studied field, existing methods primarily focus on node or graph-level tasks, leaving a gap in LP explanation techniques. This work introduces Z-REx, a GNN explanation framework designed explicitly for heterogeneous link prediction tasks. Z-REx utilizes structural and attribute perturbation to identify critical sub-structures and important features while reducing the search space by leveraging domain-specific knowledge. In our experimentation, we show the efficacy of Z-REx in generating contextually relevant and human-interpretable explanations for ZiGNN, a GNN-based recommendation engine, using a real-world real-estate dataset from Zillow Group, Inc. We also compare Z-REx to State-of-The-Art (SOTA) GNN explainers to show Z-REx's superiority in producing high-quality human-interpretable explanations.

Problem

• Enhancing interpretability of GNN for link prediction tasks
• Addressing gap in explanation techniques for recommendation systems
• Improving human-understandable explanations in real estate recommendations

Innovation

• GNN explanation framework for link prediction
• Structural and attribute perturbation technique
• Domain-specific knowledge reduces search space