推荐系统中'不嫉妒'仍不公平，因个性化导致偏好差异
Envy-Free but Still Unfair: Envy-Freeness Up To One Item (EF-1) in Personalized Recommendation
- 用'不嫉妒至一个物品'衡量公平性
- 个性化使相同推荐引发不同满意度
- 适合关注推荐公平性的研究者
envy-freeness（无嫉妒）及其放松形式EF-1（无嫉妒至一个物品）自20世纪60年代起被广泛用于经济学、博弈论与社会选择领域，并在推荐系统领域近年日益流行。本文综述了无嫉妒概念在经济学与推荐系统中的应用；并指出，在个性化扮演关键角色的场景中，嫉妒并不能准确衡量公平性，因为个体对推荐结果的偏好存在本质差异，即使满足EF-1条件，用户间仍可能产生显著的不公平感知。
原文摘要 · Abstract (English)
Envy-freeness and the relaxation to Envy-freeness up to one item (EF-1) have been used as fairness concepts in the economics, game theory, and social choice literatures since the 1960s, and have recently gained popularity within the recommendation systems communities. In this short position paper we will give an overview of envy-freeness and its use in economics and recommendation systems; and illustrate why envy is not appropriate to measure fairness for use in settings where personalization plays a role.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。