Photos Are All You Need for Reciprocal Recommendation in Online Dating

TL;DR

This paper introduces a novel reciprocal recommendation method for online dating that solely relies on user photographs, using a neural network to predict mutual preferences and significantly outperforming existing systems.

Contribution

The paper presents a new image-based reciprocal recommendation approach using neural networks, emphasizing the importance of visual data in online dating.

Findings

Achieved an F1 score of 0.87 using only photographs.

Outperformed state-of-the-art content-based and collaborative filtering methods.

Demonstrated effectiveness on a large real-world dataset.

Abstract

Recommender Systems are algorithms that predict a user's preference for an item. Reciprocal Recommenders are a subset of recommender systems, where the items in question are people, and the objective is therefore to predict a bidirectional preference relation. They are used in settings such as online dating services and social networks. In particular, images provided by users are a crucial part of user preference, and one that is not exploited much in the literature. We present a novel method of interpreting user image preference history and using this to make recommendations. We train a recurrent neural network to learn a user's preferences and make predictions of reciprocal preference relations that can be used to make recommendations that satisfy both users. We show that our proposed system achieves an F1 score of 0.87 when using only photographs to produce reciprocal recommendations on a large real world online dating dataset. Our system significantly outperforms on the state of the art in both content-based and collaborative filtering systems.