QuickSkill: Novice Skill Estimation in Online Multiplayer Games
- URL: http://arxiv.org/abs/2208.07704v1
- Date: Mon, 15 Aug 2022 11:59:05 GMT
- Title: QuickSkill: Novice Skill Estimation in Online Multiplayer Games
- Authors: Chaoyun Zhang, Kai Wang, Hao Chen, Ge Fan, Yingjie Li, Lifang Wu,
Bingchao Zheng
- Abstract summary: Current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player.
This is known as the ''cold-start'' problem for matchmaking rating algorithms.
This paper proposes QuickSKill, a deep learning based novice skill estimation framework.
- Score: 19.364132825629465
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Matchmaking systems are vital for creating fair matches in online multiplayer
games, which directly affects players' satisfactions and game experience. Most
of the matchmaking systems largely rely on precise estimation of players' game
skills to construct equitable games. However, the skill rating of a novice is
usually inaccurate, as current matchmaking rating algorithms require
considerable amount of games for learning the true skill of a new player. Using
these unreliable skill scores at early stages for matchmaking usually leads to
disparities in terms of team performance, which causes negative game
experience. This is known as the ''cold-start'' problem for matchmaking rating
algorithms.
To overcome this conundrum, this paper proposes QuickSKill, a deep learning
based novice skill estimation framework to quickly probe abilities of new
players in online multiplayer games. QuickSKill extracts sequential performance
features from initial few games of a player to predict his/her future skill
rating with a dedicated neural network, thus delivering accurate skill
estimation at the player's early game stage. By employing QuickSKill for
matchmaking, game fairness can be dramatically improved in the initial
cold-start period. We conduct experiments in a popular mobile multiplayer game
in both offline and online scenarios. Results obtained with two real-world
anonymized gaming datasets demonstrate that proposed QuickSKill delivers
precise estimation of game skills for novices, leading to significantly lower
team skill disparities and better player game experience. To the best of our
knowledge, proposed QuickSKill is the first framework that tackles the
cold-start problem for traditional skill rating algorithms.
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