Abstract — In large-scale IPTV systems, it is essential to maintain high service quality while providing a wider variety of service features than typical traditional TV. Thus service quality assessment systems are of paramount importance as they monitor the user-perceived service quality and alert when issues occurs. For IPTV systems, however, there is no simple metric to represent userperceived service quality and Quality of Experience (QoE). Moreover, there is only limited user feedback, often in the form of noisy and delayed customer calls. Therefore, we aim to approximate the QoE through a selected set of performance indicators in a proactive (i.e., detect issues before customers reports to call centers) and scalable fashion. In this paper, we present a service quality assessment framework, Q-score, which accurately learns a small set of performance indicators most relevant to user-perceived service quality, and proactively infers service quality in a single score. We evaluate Q-score using network data collected from a commercial IPTV service provider and show that Q-score is able to predict 60 % of the service problems that are reported by customers with 0.1 % false positives. Through Q-score, we have (i) gained insight into various types of service problems causing user dissatisfaction, including why users tend to react promptly to sound issues while late to video issues; (ii) identified and quantified the opportunity to proactively detect the service quality degradation of individual customers before severe performance impact occurs; and (iii) observed possibility to allocate customer care workforce to potentially troubling service areas before issues break out
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