Scholarlink

<p>Recommendation systems founded on graph neural networks (GNN) have been extensively employed because of their exceptional recommendation efficiency. Nevertheless, numerous recommendation biases also crop up, We have observed that delicate details such as gender and age are frequently implicitly apprehended by recommendation systems, culminating in unfair recommendations, and the associated algorithms of GNN will magnify this bias. To tackle these difficulties, this paper puts forth a method of introducing the notion of causal fairness into the issue of fairness in GNN-based recommendation systems, to accomplish counterfactual fairness of user-sensitive information and thereby attain unbiased recommendations. Specifically, given a GNN-based recommendation system model, which is implemented in our devised fairness framework, chiefly obtaining equitable effects through two facets: (1) attaining user embedding fairness through the counterfactual fairness technique; (2) mitigating the prejudiced impact caused by the GNN algorithm using the proposed central association subgraph method. The amalgamation of these two facets ultimately delivers unbiased recommendations. The effectiveness and sophistication of our proposed method for mitigating partiality problems in GNN recommendation systems from a causal perspective (MGRC) have been proven via experiments on four real-world datasets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A framework for solving bias in graph-based recommender systems with a causal perspective

  • 期刊论文

  • 发表日期：

    2025年2月25日

查看全文
  • Kewu Yang,
  • Guogang Li,
  • Linjia Wang,
  • Jianrong Xie

摘要

Recommendation systems founded on graph neural networks (GNN) have been extensively employed because of their exceptional recommendation efficiency. Nevertheless, numerous recommendation biases also crop up, We have observed that delicate details such as gender and age are frequently implicitly apprehended by recommendation systems, culminating in unfair recommendations, and the associated algorithms of GNN will magnify this bias. To tackle these difficulties, this paper puts forth a method of introducing the notion of causal fairness into the issue of fairness in GNN-based recommendation systems, to accomplish counterfactual fairness of user-sensitive information and thereby attain unbiased recommendations. Specifically, given a GNN-based recommendation system model, which is implemented in our devised fairness framework, chiefly obtaining equitable effects through two facets: (1) attaining user embedding fairness through the counterfactual fairness technique; (2) mitigating the prejudiced impact caused by the GNN algorithm using the proposed central association subgraph method. The amalgamation of these two facets ultimately delivers unbiased recommendations. The effectiveness and sophistication of our proposed method for mitigating partiality problems in GNN recommendation systems from a causal perspective (MGRC) have been proven via experiments on four real-world datasets.