GNNUERS:通过反事实推理在 GNN 中解释公平性以进行推荐

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Intelligent Systems and Technology Pub Date : 2024-04-03 DOI:10.1145/3655631
Giacomo Medda, Francesco Fabbri, Mirko Marras, Ludovico Boratto, Gianni Fenu
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引用次数: 0

摘要

如今,个性化研究的重点是可解释性和公平性。最近提出的几种方法能够以事后方式或通过解释路径来解释个别推荐。然而,应用于推荐中的不公平现象的可解释性技术仅限于发现用户/项目特征,而这些特征大多与有偏见的推荐有关。在本文中,我们设计了一种新颖的算法,利用反事实方法以用户-物品交互的形式发现用户不公平的解释。在我们的反事实框架中,交互被表示为双元图中的边,用户和项目则是节点。我们的双向图解释器会扰乱拓扑结构,从而找到一个改变后的版本,使受保护和不受保护人口群体之间的效用差异最小化。在来自不同领域的四个真实图上进行的实验表明,我们的方法可以系统地解释用户在三个基于 GNN 的最先进推荐模型上的不公平现象。此外,对扰动网络的经验评估发现了相关模式,证明了生成的解释所发现的不公平现象的性质。源代码和预处理数据集可在 https://github.com/jackmedda/RS-BGExplainer 上获取。
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GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning

Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations in a post-hoc manner or by explanation paths. However, explainability techniques applied to unfairness in recommendation have been limited to finding user/item features mostly related to biased recommendations. In this paper, we devised a novel algorithm that leverages counterfactuality methods to discover user unfairness explanations in the form of user-item interactions. In our counterfactual framework, interactions are represented as edges in a bipartite graph, with users and items as nodes. Our bipartite graph explainer perturbs the topological structure to find an altered version that minimizes the disparity in utility between the protected and unprotected demographic groups. Experiments on four real-world graphs coming from various domains showed that our method can systematically explain user unfairness on three state-of-the-art GNN-based recommendation models. Moreover, an empirical evaluation of the perturbed network uncovered relevant patterns that justify the nature of the unfairness discovered by the generated explanations. The source code and the preprocessed data sets are available at https://github.com/jackmedda/RS-BGExplainer.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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