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Conference proceeding
Using Collaborative Filtering to Recommend Champions in League of Legends
2020 IEEE CONFERENCE ON GAMES (IEEE COG 2020), v 2020-, pp 650-653
01 Jan 2020
Abstract
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.
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Details
- Title
- Using Collaborative Filtering to Recommend Champions in League of Legends
- Creators
- Tiffany D. Do - Univ Cent Florida, Coll Engn & Comp Sci, Orlando, FL 32816 USADylan S. Yu - The University of Texas at DallasSalman Anwer - The University of Texas at DallasSeong Ioi Wang - The University of Texas at Dallas
- Publication Details
- 2020 IEEE CONFERENCE ON GAMES (IEEE COG 2020), v 2020-, pp 650-653
- Series
- IEEE Conference on Computational Intelligence and Games
- Publisher
- IEEE
- Number of pages
- 4
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000632592300093
- Scopus ID
- 2-s2.0-85096934117
- Other Identifier
- 991021916803904721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Software Engineering