基于Steam用户特征的异常玩家识别方法及其游戏障碍风险分析

2023 11th International Conference on Information and Education Technology (ICIET) Pub Date : 2023-03-18 DOI:10.1109/ICIET56899.2023.10111303

Chengyu Jin, Mohd Anuaruddin Bin Ahmadon, S. Yamaguchi

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引用次数: 0

摘要

在本文中，我们提出了一个基于游戏属性和玩家属性的评级指标来确定游戏的游戏障碍风险。我们从热门游戏平台Steam上收集了20款游戏的24,950个活跃用户数据。使用无监督机器学习聚类方法-DBSCAN和游戏障碍流行指数作为阈值，我们发现游戏障碍风险与它们的受欢迎程度无关。然而，它与“开放世界”、“动作”和“多人模式”等游戏功能的关系更大。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Method of Distinguishing Abnormal Player Based on Steam User Profile for Game Disorder Risk Analysis

In this paper, we proposed a rating index to determine the game-disorder risk of a game based on game attributes and player attributes. We collected 24,950 active user profile data for 20 games from a popular game platform called Steam. Using an unsupervised machine learning clustering approach -DBSCAN and game-disorder prevalence index as its threshold, we found that the game-disorder risk is not concerned with their popularity. However, it has more relation to game features such as "Open World", "Action" and "Multiplayer".

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来源期刊

2023 11th International Conference on Information and Education Technology (ICIET)

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