提出MODE方法，让匹配平台推荐更公平且高效。
MODE: Mutual Optimality in Direct Effects of Reciprocal Recommendations in Matching Markets
- 基于双向用户偏好，优化推荐列表的直接效果。
- 实验显示可提升匹配成功率并加快处理速度。
- 适合需平衡公平与效率的匹配平台设计者。
近年来，求职招聘和在线婚恋等匹配平台广泛应用。平台成功的关键在于设计合理的互惠推荐系统（RRS），兼顾双方用户（如求职者与雇主）的偏好，并避免资源过度集中于少数热门用户。然而，过度强调分散性可能导致对部分用户的推荐质量下降，引发不满。本文提出“直接效果最优性”概念，衡量单个用户推荐结果在他人推荐影响下的最优性，并提出新型方法MODE，实现双向推荐的相互最优。在合成数据与真实世界数据上的实验表明，MODE在直接效果互惠性方面优于现有方法，处理速度更快，且能带来更高的预期匹配数量。
原文摘要 · Abstract (English)
Matching platforms such as job posting services and online dating platforms have become widely used over the past decade. For a matching platform to be successful, it is crucial to design appropriate reciprocal recommendation systems (RRSs) that consider the preferences of users on both sides (job candidates and employers) and prevent opportunities from being concentrated too heavily on a few popular users. However, prioritizing concentration mitigation too much can lead to recommending undesirable results to some individual users, resulting in their dissatisfaction. In this paper, we formulate the concept of ``optimality of direct effects'' of the recommendation list for an individual user, given the recommendations to other users. Furthermore, we propose a novel method, MODE, that computes mutually optimal recommendations in direct effects. Experiments with synthetic and real-world data demonstrate that MODE surpasses other existing methods in terms of mutual optimality of direct effects, exhibits faster processing speeds, and enables a higher expected number of matches.
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