Using Collaborative Filtering to Recommend Champions in League of Legends
This addresses champion selection for players in a popular game, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of recommending champions in League of Legends by proposing a collaborative filtering system using singular value decomposition, and results from a user study showed players significantly preferred its recommendations over random ones.
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.