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.

翻译：在线约会平台已从根本上改变了浪漫关系的形成方式，全球数百万用户依赖算法匹配系统寻找合适伴侣。然而，当前约会应用中的推荐系统存在显著的算法缺陷，包括但不限于流行度偏见、过滤气泡效应以及互惠建模不足等问题，这些缺陷限制了系统效能并引入了有害偏见。本研究整合基础理论与最新实证发现，对约会应用推荐系统进行详细分析，揭示关键问题并提出基于研究证据的解决方案。通过对互惠推荐框架、公平性评估指标及行业实施方案的分析，我们证明当前系统仅取得有限性能——协同过滤方法达到25.1%，而互惠方法达到28.7%。我们提出的数学框架通过增强相似性度量、多元目标优化和公平感知算法来解决这些局限性，在保持竞争力的准确率同时，通过提升人口统计表征度来降低算法偏见。