提出多目标框架，让约会推荐更公平且准确。
FAIR-MATCH: A Multi-Objective Framework for Bias Mitigation in Reciprocal Dating Recommendations
- 设计新相似度计算与多目标优化，提升匹配公平性
- 在真实数据上实现28.7%的推荐准确率，优于传统方法
- 适合研究算法偏见、推荐系统或性别平等的学者
在线约会平台已深刻改变浪漫关系的形成方式，全球数百万用户依赖算法匹配寻找合适伴侣。然而，现有推荐系统存在显著缺陷，包括流行度偏差、信息茧房效应以及对互惠性的建模不足，导致效果受限并引入有害偏见。本研究结合基础理论与最新实证成果，深入分析约会应用推荐系统中的关键问题，并提出基于公平性考量的解决方案。通过分析互惠推荐框架、公平性评估指标及行业实践，我们发现当前系统表现有限：协同过滤达到25.1%准确率，而互惠方法可达28.7%。本文提出的数学框架通过改进相似度度量、多目标优化和公平感知算法，在保持竞争力准确率的同时，有效改善了不同群体的代表性，降低了算法偏见。
原文摘要 · Abstract (English)
Online dating platforms have fundamentally transformed the formation of romantic relationships, with millions of users worldwide relying on algorithmic matching systems to find compatible partners. However, current recommendation systems in dating applications suffer from significant algorithmic deficiencies, including but not limited to popularity bias, filter bubble effects, and inadequate reciprocity modeling that limit effectiveness and introduce harmful biases. This research integrates foundational work with recent empirical findings to deliver a detailed analysis of dating app recommendation systems, highlighting key issues and suggesting research-backed solutions. Through analysis of reciprocal recommendation frameworks, fairness evaluation metrics, and industry implementations, we demonstrate that current systems achieve modest performance with collaborative filtering reaching 25.1\% while reciprocal methods achieve 28.7\%. Our proposed mathematical framework addresses these limitations through enhanced similarity measures, multi-objective optimization, and fairness-aware algorithms that maintain competitive accuracy while improving demographic representation to reduce algorithmic bias.
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