The Internet service provider industry is currently experiencing intense competition as companies strive to provide top-notch services to their customers. Providers are introducing cutting-edge technologies to enhance service quality, understanding that their survival depends on the level of service they offer. However, evaluating service quality is a complex task. A crucial aspect of this evaluation lies in understanding user experience, which significantly impacts the success and reputation of a service or product. Ensuring a seamless and positive user experience is essential for attracting and retaining customers. To date, much effort has been devoted to developing tools for measuring Quality of Experience (QoE), which incorporate both subjective and objective criteria. These tools, available in closed and open-source formats, are accessible to organizations and contribute to improving user experience quality. This review article delves into recent research and initiatives aimed at creating frameworks for assessing user QoE. It also explores the integration of machine learning algorithms to enhance these tools for future advancements. Additionally, the article examines current challenges and envisions future directions in the development of these measurement tools.

翻译：互联网服务提供商行业正经历激烈竞争，各企业竭力为用户提供顶级服务。服务提供商纷纷引入尖端技术提升服务质量，深知其生存取决于服务水平。然而，服务质量评估是一项复杂任务。该评估的关键在于理解用户体验，这直接影响服务或产品的成功与声誉。确保流畅积极的用户体验对吸引和留住客户至关重要。迄今为止，大量研究致力于开发融合主观与客观标准的体验质量（QoE）测量工具。这些工具以闭源和开源形式存在，可供组织使用并助力提升用户体验质量。本综述深入探讨了近期旨在构建用户QoE评估框架的研究与举措，同时分析了机器学习算法在增强上述工具以推动未来进步中的应用。此外，本文还审视了当前挑战，并展望了这些测量工具的发展方向。