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首页> 外文会议>International Conference on Digital Telecommunications >Likelihood to Recommend (L2R) Prediction using Quality of Experience (QoE) Measurements: A Longitudinal Study
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Likelihood to Recommend (L2R) Prediction using Quality of Experience (QoE) Measurements: A Longitudinal Study

机译：推荐（L2R）预测的可能性使用经验质量（QoE）测量：纵向研究

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摘要

Models that predict satisfaction with a service over time need to consider the impact of emotions and remembered quality of experience in creating attitudes towards a service. However, prior research on subjective quality of experience has typically focused on experiments conducted in a single session or over a short period of time. Thus, there is a gap between our understanding of instantaneous quality of experience and long-term judgments, such as overall satisfaction and likelihood to recommend and likelihood to churn. The goal of the study in this paper was to carry out a longitudinal study that would provide initial insights into how experiences of service quality over time are mediated through emotions and memory and accumulated into longer term attitudes about the service. The longitudinal study was carried out over a period of roughly 4 weeks with around 3 sessions per week. A specially constructed online service was used where participants could select YouTube videos to view, and the service would randomly add impairments to the videos before playing back the videos and then asking questions relating to Quality of Experience, Technical Quality and overall frustration and satisfaction. In this paper, we report on the results obtained from the first 8 sessions of data.
机译：随着时间的推移预测服务的满足的模型需要考虑情绪的影响，并记住为服务创造态度的体验质量。然而，关于主观经验质量的先验研究通常集中在单一会议或短时间内进行的实验。因此，我们对瞬间经验质量和长期判断的理解之间存在差距，例如对推荐和可能性的建议和可能性的总体满意度和可能性。本文的研究目的是进行纵向研究，可以通过情绪和记忆介导的服务质量随着时间的推移以及累积对服务的长期态度来提供初步的洞察。纵向研究在大约4周的时间内进行，每周大约3次。使用专门构造的在线服务，参与者可以选择YouTube视频来查看，并且该服务将在播放视频之前随机添加障碍，然后询问有关经验质量，技术质量和整体挫折和满意的问题。在本文中，我们报告了从前8个数据会议获得的结果。

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