Data mining for item recommendation in MOBA games

Abstract

E-Sports has been positioned as an important activity within MOBA (Multiplayer Online Battle Arena) games in recent years. There is existing research on recommender systems in this topic, but most of it focuses on the character recommendation problem. However, the recommendation of items is also challenging because of its contextual nature, depending on the other characters. We have developed a framework that suggests items for a character based on the match context. The system aims to help players who have recently started the game as well as frequent players to take strategic advantage during a match and to improve their purchasing decision making. By analyzing a dataset of ranked matches through data mining techniques, we can capture purchase dynamic of experienced players to use it to generate recommendations. The results show that our proposed solution yields up to 80% of mAP, suggesting that the method leverages context information successfully. These results, together with open issues we mention in the paper, call for further research in the area.