MODE：匹配市场中互惠推荐直接效应的相互最优性
MODE: Mutual Optimality in Direct Effects of Reciprocal Recommendations in Matching Markets
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中文总结 AI 辅助
本文针对匹配平台的互惠推荐系统，提出MODE方法计算直接效应的相互最优推荐，实验显示其在直接效应最优性、处理速度和预期匹配数上优于现有方法。
中文摘要 AI 辅助
过去十年，求职平台、在线交友平台等匹配平台得到广泛应用。匹配平台要取得成功，设计合适的互惠推荐系统（RRS）至关重要，该系统需考虑双方用户（求职者与雇主）的偏好，避免机会过度集中在少数热门用户身上。但若过度侧重缓解机会集中，会向部分用户推荐不合意的结果，导致其不满。本文在给定其他用户推荐结果的前提下，为单个用户的推荐列表定义了“直接效应最优性”的概念，并提出一种计算直接效应相互最优推荐的新方法MODE。在合成数据和真实数据上的实验表明，MODE在直接效应的相互最优性上优于其他现有方法，处理速度更快，还能提升预期匹配数量。
英文摘要
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.
