Predicting Virtual World User Population Fluctuations with Deep Learning

被引：3

作者：

Kim, Young Bin ^{[1
]}

Park, Nuri ^{[2
]}

Zhang, Qimeng ^{[1
]}

Kim, Jun Gi ^{[3
]}

Kang, Shin Jin ^{[3
]}

Kim, Chang Hun ^{[2
]}

机构：

[1] Korea Univ, Interdisciplinary Program Visual Informat Proc, Seoul, South Korea

[2] Korea Univ, Dept Comp & Radio Commun Engn, Seoul, South Korea

[3] Hongik Univ, Sch Games, Seoul, South Korea

来源：

PLOS ONE | 2016年 / 11卷 / 12期

基金：

新加坡国家研究基金会;

关键词：

GAME; FORECASTS; NETWORKS; PATTERNS; BEHAVIOR; LIFE;

D O I：

10.1371/journal.pone.0167153

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.

引用

页数：12

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