Matching Theory-based Recommender Systems in Online Dating
Yoji Tomita, Riku Togashi, Daisuke Moriwaki
TL;DR
This paper explores the integration of matching theory into recommender systems for online dating, aiming to improve partner suggestions by leveraging theoretical models in a real-world application.
Contribution
It introduces a novel matching theory-based recommender system (MTRS) tailored for online dating platforms, bridging theoretical models with practical deployment.
Findings
Potential for improved matching accuracy
Enhanced understanding of reciprocal preferences
Successful deployment in a real-world platform
Abstract
Online dating platforms provide people with the opportunity to find a partner. Recommender systems in online dating platforms suggest one side of users to the other side of users. We discuss the potential interactions between reciprocal recommender systems (RRSs) and matching theory. We present our ongoing project to deploy a matching theory-based recommender system (MTRS) in a real-world online dating platform.
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