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 large-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 fast and examination-model-free 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 a large-scale dataset for which one existing method does not work.

翻译：在招聘平台和在线约会等匹配市场中，推荐系统对平台的成功至关重要。与向用户推荐物品的标准推荐系统不同，互惠推荐系统（RRS）需要推荐其他用户，因此必须考虑用户之间的相互兴趣。此外，确保推荐机会不会过度偏向热门用户，对于匹配总数和用户间公平性至关重要。然而，现有匹配市场的推荐方法在大规模平台上存在计算挑战，并且依赖于位置模型（PBM）中的特定检视函数。本文基于匹配市场排序推荐中的可转移效用匹配（TU匹配）模型，提出了一种快速且免检视模型的互惠推荐方法。此外，我们在日本某在线约会平台的合成数据和真实数据上进行了实验评估。在匹配总数方面，我们的方法优于或持平于现有方法，即使在一个现有方法无法工作的大规模数据集上，也能取得良好效果。