Predicting Virtual World User Population Fluctuations with Deep Learning

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التفاصيل البيبلوغرافية
الحاوية / القاعدة:	PLoS One vol. 11, no. 12 (Dec 2016), p. e0167153
المؤلف الرئيسي:	Young Bin Kim
مؤلفون آخرون:	Park, Nuri, Zhang, Qimeng, Kim, Jun Gi, Shin Jin Kang, Kim, Chang Hun
منشور في:	Public Library of Science
الموضوعات:	Korea University Wikipedia Radio Communications Economic Research Radio communications International conferences Population Computer & video games Internet Trends Signal processing Interdisciplinary aspects Researchers Virtual communities Community Fluctuations Use statistics Populations Studies Information processing Data collection Acoustics Economic activity Deep learning Datasets
الوصول للمادة أونلاين:	Citation/Abstract Full Text Full Text - PDF
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الوصف
مستخلص:	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.
تدمد:	1932-6203
DOI:	10.1371/journal.pone.0167153
المصدر:	Health & Medical Collection