Authors:
Tatsuro Ide
1
and
Hiroshi Hosobe
2
Affiliations:
1
Graduate School of Computer and Information Sciences, Hosei University, Tokyo, Japan
;
2
Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan
Keyword(s):
Online Cooperative Game, Team Matching, Statistical Analysis, Visualization.
Abstract:
Although the COVID-19 pandemic has increased people demanding to play online cooperative games with others, in-game random team matching has not fully supported it. Furthermore, toxic behaviors such as verbal abuse and trolling by randomly gathered team members adversely affect user experience. Public Discord servers and game-specific team matching services are often used to support this problem from outside the game. However, in both services, players can obtain only a few lines of other players’ self-introductions before playing together, and therefore their anxiety about possible mismatches is a major obstacle to the use of these services. In this paper, we aim to support team matching in an online cooperative game from both aspects of players’ personalities and skills. Especially, we perform team member recommendation based on the visualization of in-game statistical information by computing players’ personalities and skills from their game masteries and character preferences in
a typical game called VALORANT.
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