# Using Collaborative Filtering to Recommend Champions in League of Legends

arXiv: https://arxiv.org/abs/2006.10191 · HTML: https://mlsanity.com/paper/2006.10191/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

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

- [Using Machine Learning to Predict Game Outcomes Based on Player-Champion Experience in League of Legends](https://mlsanity.com/paper/2108.02799/) (2021)

## Abstract (arXiv)

League of Legends (LoL), one of the most widely played computer games in the world, has over 140 playable characters known as champions that have highly varying play styles. However, there is not much work on providing champion recommendations to a player in LoL. In this paper, we propose that a recommendation system based on a collaborative filtering approach using singular value decomposition provides champion recommendations that players enjoy. We discuss the implementation behind our recommendation system and also evaluate the practicality of our system using a preliminary user study. Our results indicate that players significantly preferred recommendations from our system over random recommendations.