In recent years, there has been a substantial increase in the distribution of videos over the Internet, and this has become one of the major activities that attract extensive attention. This means that users expect to watch videos of the highest quality. Quality of experience (QoE) describes the degree of satisfaction or annoyance of a user when they are using a multimedia service or application. Meeting users’ expectations requires understanding the factors that influence QoE and efficiently managing resources to optimize video quality. The current objective approaches that assess QoE mostly rely on the analysis of video traffic. However, recent research has demonstrated that this approach cannot sufficiently evaluate perceived QoE and that multiple factors, including media technical features, influence QoE. It is crucial for service providers to identify the effects of social context, in addition to those of user-related, content-related, and system factors, on perceived QoE of the end user. Recent studies have focused on understanding the characteristics of user behavior and engagement, as well as the effect of these factors on QoE. In this study, we use social context factors and user engagement as subjective factors to structure a user QoE evaluation model. First, we study social context factors and user engagement characteristics and investigate their correlation with QoE. Next, we build a metric that estimates the end-to-end QoE for a specific aspect of user actions. Then, by simulating mathematical metrics, we employ machine learning models to predict QoE; finally, we validate this approach using metrics for statistical evaluation of quality prediction models.

近年来，视频在Internet上的分发已大大增加，这已成为引起广泛关注的主要活动之一。这意味着用户希望观看最高质量的视频。体验质量（QoE）描述了用户使用多媒体服务或应用程序时的满意度或烦恼程度。要满足用户的期望，就必须了解影响QoE的因素，并有效地管理资源以优化视频质量。当前评估QoE的客观方法主要依赖于视频流量的分析。但是，最近的研究表明，这种方法不能充分评估感知到的QoE，并且包括媒体技术特征在内的多种因素也会影响QoE。对于服务提供商而言，至关重要的是，除了要确定与用户相关的，与内容相关的以及系统因素之外，还要确定社交环境对最终用户感知QoE的影响。最近的研究集中在理解用户行为和参与的特征，以及这些因素对QoE的影响。在这项研究中，我们使用社会情境因素和用户参与度作为主观因素来构建用户QoE评估模型。首先，我们研究社交情境因素和用户参与度特征，并研究它们与QoE的相关性。接下来，我们建立一个度量标准，以评估用户操作特定方面的端到端QoE。然后，通过模拟数学指标，我们采用机器学习模型来预测QoE；最后，