In matching markets such as job posting and online dating platforms, the recommender system plays a critical role in the success of the platform. Unlike standard recommender systems that suggest items to users, reciprocal recommender systems (RRSs) that suggest other users must take into account the mutual interests of users. In addition, ensuring that recommendation opportunities do not disproportionately favor popular users is essential for the total number of matches and for fairness among users. Existing recommendation methods in matching markets, however, face computational challenges on real-world scale platforms and depend on specific examination functions in the position-based model (PBM). In this paper, we introduce the reciprocal recommendation method based on the matching with transferable utility (TU matching) model in the context of ranking recommendations in matching markets, and propose a faster and examination-agnostic algorithm. Furthermore, we evaluate our approach on experiments with synthetic data and real-world data from an online dating platform in Japan. Our method performs better than or as well as existing methods in terms of the total number of matches and works well even in relatively large datasets for which one existing method does not work.

翻译：在招聘网站和在线约会平台等匹配市场中，推荐系统对平台的成功起着关键作用。与向用户推荐物品的标准推荐系统不同，互惠推荐系统（RRSs）必须考虑用户之间的相互兴趣，从而向用户推荐其他用户。此外，确保推荐机会不会过度偏向热门用户，对于总匹配数以及用户间的公平性至关重要。然而，现有匹配市场中的推荐方法在应用于现实规模平台时面临计算挑战，且依赖于位置模型（PBM）中特定的检验函数。本文引入基于可转移效用匹配（TU matching）模型的互惠推荐方法，用于匹配市场中的排名推荐场景，并提出一种速度更快且无需检验函数的算法。此外，我们使用合成数据以及来自日本某在线约会平台的真实数据对方法进行评估。实验结果表明，在总匹配数指标上，我们的方法优于或持平现有方法，即使对于现有某方法无法处理的大规模数据集，也能取得良好表现。