This article proposes a novel approach for enhancing the video popularity prediction models. Using the proposed approach, we enhance three popularity prediction techniques that outperform the accuracy of the prior state-of-the-art solutions. The major components of the proposed approach are two novel mechanisms for ” user grouping ” and ” content classification .” The user grouping method is an unsupervised clustering approach that divides the users into an adequate number of user groups with similar interests. The content classification approach identifies the classes of videos with similar popularity growth trends. To predict the popularity of the newly-released videos, our proposed popularity prediction model trains its parameters in each user group and its associated video popularity classes. Evaluations are performed through a 5-fold cross validation and on a dataset containing one month video request records of 26,706 users of BBC iPlayer. Using the proposed grouping technique, user groups of similar interest and up to two video popularity classes for each user group were detected. Our analysis shows that the accuracy of the proposed solution outperforms the state-of-the-art, including Szabo-Huberman (SH), Multivariate Linear (ML), and Multivariate linear Radial Basis Functions (MRBF) models by an average of 45%, 33%, and 24%, respectively. Finally, we discuss how various systems in the network and service management domain such as cache deployment, advertising, and video broadcasting technologies benefit from our findings to illustrate the implications.

中文翻译：

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通过用户分组和视频流行度分类提高视频流行度预测模型的准确性

本文提出了一种增强视频流行度预测模型的新方法。使用所提出的方法，我们增强了三种流行度预测技术，这些技术的准确性优于先前最先进解决方案的准确性。所提出方法的主要组成部分是两种新机制，用于“用户分组“ 和 ”内容分类。” 用户分组方法是一种无监督聚类方法，将用户划分为足够数量的具有相似兴趣的用户组。内容分类方法识别具有相似流行度增长趋势的视频类别。为了预测新发布视频的流行度，我们提出的流行度预测模型在每个用户组及其相关的视频流行度类别中训练其参数。评估通过 5 折交叉验证和包含 BBC iPlayer 的 26,706 名用户的一个月视频请求记录的数据集进行。使用所提出的分组技术，检测到具有相似兴趣的用户组和每个用户组最多两个视频流行度类别。我们的分析表明，所提出的解决方案的准确性优于最先进的解决方案，包括 Szabo-Huberman (SH)、多元线性 (ML) 和多元线性径向基函数 (MRBF) 模型，平均分别提高了 45%、33% 和 24%。最后，我们讨论了网络和服务管理领域中的各种系统，如缓存部署、广告和视频广播技术如何从我们的研究结果中受益，以说明其含义。