# Photos Are All You Need for Reciprocal Recommendation in Online Dating

arXiv: https://arxiv.org/abs/2108.11714 · HTML: https://mlsanity.com/paper/2108.11714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Ask the GRU: Multi-Task Learning for Deep Text Recommendations](https://mlsanity.com/paper/1609.02116/) (2016)
- [Reciprocal Recommendation System for Online Dating](https://mlsanity.com/paper/1501.06247/) (2015)
- [Comparative Deep Learning of Hybrid Representations for Image Recommendations](https://mlsanity.com/paper/1604.01252/) (2016)
- [Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information](https://mlsanity.com/paper/1605.09062/) (2016)

## Abstract (arXiv)

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.