In today's world, the Internet is recognized as one of the essentials of human life, playing a significant role in communications, business, and lifestyle. The quality of internet services can have widespread negative impacts on individual and social levels. Consequently, Quality of Service (QoS) has become a fundamental necessity for service providers in a competitive market aiming to offer superior services. The success and survival of these providers depend on their ability to maintain high service quality and ensure satisfaction.Alongside QoS, the concept of Quality of Experience (QoE) has emerged with the development of telephony networks. QoE focuses on the user's satisfaction with the service, helping operators adjust their services to meet user expectations. Recent research shows a trend towards utilizing machine learning and deep learning techniques to predict QoE. Researchers aim to develop accurate models by leveraging large volumes of data from network and user interactions, considering various real-world scenarios. Despite the complexity of network environments, this research provides a practical framework for improving and evaluating QoE. This study presents a comprehensive framework for evaluating QoE in multimedia services, adhering to the ITU-T P.1203 standard which includes automated data collection processes and uses machine learning algorithms to predict user satisfaction based on key network parameters. By collecting over 20,000 data records from different network conditions and users, the Random Forest model achieved a prediction accuracy of 95.8% for user satisfaction. This approach allows operators to dynamically allocate network resources in real-time, maintaining high levels of customer satisfaction with minimal costs.

翻译：在当今世界，互联网已被视为人类生活的基本要素之一，在通信、商业和生活方式中发挥着重要作用。互联网服务质量可能对个人和社会层面产生广泛的负面影响。因此，服务质量已成为竞争市场中服务提供商提供优质服务的基本需求。这些提供商的成功与生存取决于其维持高服务质量和确保用户满意的能力。随着电话网络的发展，在服务质量之外出现了体验质量的概念。体验质量关注用户对服务的满意度，帮助运营商调整服务以满足用户期望。最近的研究显示出利用机器学习和深度学习技术预测体验质量的趋势。研究人员旨在利用来自网络和用户交互的大量数据，考虑各种现实场景，开发精确的预测模型。尽管网络环境复杂，这项研究为改进和评估体验质量提供了一个实用框架。本研究提出了一个用于评估多媒体服务体验质量的综合框架，该框架遵循ITU-T P.1203标准，包含自动化数据收集流程，并使用机器学习算法基于关键网络参数预测用户满意度。通过从不同网络条件和用户收集超过20,000条数据记录，随机森林模型在用户满意度预测方面达到了95.8%的准确率。该方法使运营商能够实时动态分配网络资源，以最低成本维持高水平的客户满意度。