Fairness-Aware Explainable Recommendation over Knowledge Graphs

Abstract

There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in particular, may suffer from both explanation bias and performance disparity. In this paper, we analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations, due to insufficient training data for the inactive users, and that their recommendations may be biased by the training records of more active users, due to the nature of collaborative filtering, which leads to an unfair treatment by the system. We propose a fairness constrained approach via heuristic re-ranking to mitigate this unfairness problem in the context of explainable recommendation over knowledge graphs. We experiment on several real-world datasets with state-of-the-art knowledge graph-based explainable recommendation algorithms. The promising results show that our algorithm is not only able to provide high-quality explainable recommendations, but also reduces the recommendation unfairness in several respects.

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• KGAT: Knowledge Graph Attention Network for Recommendation
• Equality of Opportunity in Supervised Learning
• Counterfactual Fairness
• Fairness of Exposure in Rankings
• FA*IR: A Fair Top-k Ranking Algorithm
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• Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
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• Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation

Cited by in corpus (6)

• User-oriented Fairness in Recommendation
• Towards Long-term Fairness in Recommendation
• CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
• Learning Post-Hoc Causal Explanations for Recommendation
• Personalized Transformer for Explainable Recommendation
• Grouping Search Results with Product Graphs in E-commerce Platforms