# User recommendation in reciprocal and bipartite social networks -- a case study of online dating

arXiv: https://arxiv.org/abs/1311.2526 · HTML: https://mlsanity.com/paper/1311.2526/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2013.

## Built on by (most cited first, 1 of 1)

- [Automated Polynomial Filter Learning for Graph Neural Networks](https://mlsanity.com/paper/2307.07956/) (2023)

## Abstract (arXiv)

Many social networks in our daily life are bipartite networks built on reciprocity. How can we recommend users/friends to a user, so that the user is interested in and attractive to recommended users? In this research, we propose a new collaborative filtering model to improve user recommendations in reciprocal and bipartite social networks. The model considers a user's "taste" in picking others and "attractiveness" in being picked by others. A case study of an online dating network shows that the new model has good performance in recommending both initial and reciprocal contacts.