MODE: Mutual Optimality in Direct Effects of Reciprocal Recommendations in Matching Markets

2026-08-03

AI Summary

This work addresses the problem of excessive concentration on popular users in matching platforms, which undermines individual satisfaction and system efficiency. The authors introduce the novel concept of “direct-effect optimality” to formally characterize the immediate impact of recommendations on users and propose MODE, a method that generates globally mutually optimal recommendation lists under bilateral preference constraints. Built upon optimization theory, MODE formulates a reciprocal recommendation model that efficiently balances fairness and utility under multi-sided constraints. Experimental results demonstrate that MODE significantly outperforms existing approaches in terms of mutual optimality, number of successful matches, and computational efficiency.

Abstract

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.

Problem

• reciprocal recommendations
• matching markets
• concentration mitigation
• user satisfaction
• direct effects

Innovation

• mutual optimality
• reciprocal recommendation systems
• matching markets
• direct effects
• MODE