# Network-based recommendation algorithms: A review

arXiv: https://arxiv.org/abs/1511.06252 · HTML: https://mlsanity.com/paper/1511.06252/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2015.

## Built on by (most cited first, 3 of 3)

- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Recommender Systems for Online and Mobile Social Networks: A survey](https://mlsanity.com/paper/2307.01207/) (2023)
- [LaSER: Language-Specific Event Recommendation](https://mlsanity.com/paper/2303.04712/) (2023)

## Abstract (arXiv)

Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use - such as the possible influence of recommendation on the evolution of systems that use it - and finally discuss open research directions and challenges.