The Web is one of the most successful Internet applications. Yet, the quality of Web users’ experience is still largely impenetrable. Whereas Web performance is typically studied with controlled experiments, in this work we perform a large-scale study of a real site, Wikipedia, explicitly asking (a small fraction of its) users for feedback on the browsing experience. The analysis of the collected feedback reveals that 85% of users are satisfied, along with both expected (e.g., the impact of browser and network connectivity) and surprising findings (e.g., absence of day/night, weekday/weekend seasonality) that we detail in this paper. Also, we leverage user responses to build supervised data-driven models to predict user satisfaction which, despite including state-of-the art quality of experience metrics, are still far from achieving accurate results (0.62 recall of negative answers). Finally, we make our dataset publicly available, hopefully contributing in enriching and refining the scientific community knowledge on Web users’ QoE.

中文翻译：

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分析维基百科用户感知的体验质量：一项大规模研究

Web 是最成功的 Internet 应用程序之一。然而，Web 用户体验的质量在很大程度上仍然是难以理解的。虽然 Web 性能通常是通过受控实验来研究的，但在这项工作中，我们对真实网站 Wikipedia 进行了大规模研究，明确询问（其一小部分）用户对浏览体验的反馈。对收集到的反馈的分析表明，85% 的用户感到满意，以及我们详细说明的预期（例如，浏览器和网络连接的影响）和令人惊讶的发现（例如，没有白天/黑夜，工作日/周末季节性）在本文中。此外，我们利用用户响应来构建受监督的数据驱动模型来预测用户满意度，尽管包括最先进的体验质量指标，还远未达到准确的结果（否定答案的召回率为 0.62）。最后，我们公开了我们的数据集，希望有助于丰富和完善有关 Web 用户 QoE 的科学社区知识。