Recommender System for Online Dating Service

TL;DR

This paper presents a recommender system for online dating that improves user experience by outperforming global algorithms, with collaborative filtering methods preferred by users, demonstrating potential for enhanced service value.

Contribution

The paper introduces a recommender system for online dating and compares collaborative filtering algorithms with global algorithms, showing CF's superior performance and user preference.

Findings

Collaborative filtering recommenders outperform global algorithms.

Users prefer CF-based recommendations over global popularity.

Recommender systems can enhance online dating services' value.

Abstract

Users of online dating sites are facing information overload that requires them to manually construct queries and browse huge amount of matching user profiles. This becomes even more problematic for multimedia profiles. Although matchmaking is frequently cited as a typical application for recommender systems, there is a surprising lack of work published in this area. In this paper we describe a recommender system we implemented and perform a quantitative comparison of two collaborative filtering (CF) and two global algorithms. Results show that collaborative filtering recommenders significantly outperform global algorithms that are currently used by dating sites. A blind experiment with real users also confirmed that users prefer CF based recommendations to global popularity recommendations. Recommender systems show a great potential for online dating where they could improve the value of the service to users and improve monetization of the service.