Date of Award
2022
Document Type
Thesis
Degree Name
Bachelors
Department
Natural Sciences
First Advisor
Gillman, David
Area of Concentration
Computer Science
Abstract
The video game DotA 2 has a steep learning curve. Choosing which character (or “hero”) and position to play is not obvious for new players. This thesis proposes a recommender system that chooses a hero and position for a player based on their style of play. This system works by matching the player with experts who play similarly. The system is intended to make the game more enjoyable for beginners and to increase player retention. In this work, we present a dataset containing the actions of expert players representing different heroes in different positions and situations over many thousands of games. We present the results of clustering algorithms applied to heroes and positions according to their actions and applied to the actions themselves for feature selection. We present the results of an initial machine learning model that uses these features to predict hero and position from a player’s style of play.
Recommended Citation
Flint, Trevor, "CREATING A HERO RECOMMENDER SYSTEM FOR NEWER PLAYER IN DOTA 2" (2022). Theses & ETDs. 6177.
https://digitalcommons.ncf.edu/theses_etds/6177