Enhanced prediction of user-preferred YouTube videos based on cleaned viewing pattern history
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Date
2017
Authors
Braun, Peter
Cuzzocrea, Alfredo
Doan, Lam M.V.
Kim, Suyoung
Leung, Carson K.
Matundan, Jose Francisco A.
Singh, Rashpal Robby
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
In current era of big data, a wide variety of high-volume data having different veracity can be easily collected or generated at a high velocity. Social network data, as well as audio and video in social media and social networking sites, are examples of big data. Embedded in these big data are valuable information and knowledge. To discovery this implicit, previously unknown and potentially useful information and knowledge from these big data, some big data science solutions are in demand. In this paper, we explore big data mining techniques for detecting outliers or anomalies from YouTube video viewing history and data-cleaning this viewing log so that the user-preferred YouTube viewing patterns or trends can be recognized and the prediction of user-preferred YouTube videos can then be enhanced.
Description
P. Braun, A. Cuzzocrea, L.M.V. Doan, S. Kim, C.K. Leung, J.F.A. Matundan, R.R. Singh. Enhanced prediction of user-preferred YouTube videos based on cleaned viewing pattern history. Procedia Computer Science, 112 (2017), pp. 2230-2239. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords
data mining, outlier detection, anomaly detection, YouTube, video viewing history, trends, prediction, applications, knowledge engineering, large-scale systems
Citation
P. Braun, A. Cuzzocrea, L.M.V. Doan, S. Kim, C.K. Leung, J.F.A. Matundan, R.R. Singh. Enhanced prediction of user-preferred YouTube videos based on cleaned viewing pattern history. Procedia Computer Science, 112 (2017), pp. 2230-2239