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Classification of Viewing Abandonment Reasons for Adaptive Bitrate Streaming

机译：自适应比特率流的观看放弃原因的分类

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As adaptive bitrate streaming services have spread, it has become more important for video streaming providers to control video quality and prevent viewing abandonments. However, since viewing abandonments are caused not only by quality degradations but also by a lack of users' interest in contents, it will first be necessary to clarify how quality and/or content affect viewing abandonments. To investigate this, we conducted an adaptive bitrate streaming experiment and developed a viewing-abandonment-reason-classification model that classifies abandonment reasons into quality or content. Using training data, we developed four models (logistic regression, classification tree, random forests, and support vector machine) where feature variables related to application quality, users' operation behaviors, and the attributes of viewed contents were used as explanatory variables. These four models were validated by using validation data. From the results, the support vector machine model was considered to be the best since it obtained relatively good validation results and did not appear to be over-trained.
机译：随着自适应比特率流媒体服务的普及，对于视频流媒体提供商来说，控制视频质量并防止观看遗弃变得越来越重要。但是，由于观看放弃不仅是由于质量下降引起的，而且是由于用户对内容缺乏兴趣引起的，因此首先有必要弄清质量和/或内容如何影响观看放弃。为了对此进行调查，我们进行了自适应比特率流媒体实验，并开发了一种查看放弃原因分类模型，该模型将放弃原因分为质量或内容。利用训练数据，我们开发了四个模型（逻辑回归，分类树，随机森林和支持向量机），其中与应用程序质量，用户的操作行为和查看内容的属性有关的特征变量用作解释变量。通过使用验证数据对这四个模型进行了验证。从结果来看，支持向量机模型被认为是最好的，因为它获得了相对较好的验证结果，并且似乎没有受到过度训练。

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