Towards Identifying Impacted Users in Cellular Services
Towards Identifying Impacted Users in Cellular Services
复制标题
识别蜂窝服务中受影响的用户
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Jia Wang
中科院分区：
文献类型：
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作者：
Shobha Venkataraman;Jia Wang
手机服务运营商客户服务的一个重要步骤是确定个人用户是否受到持续服务问题的影响。这通常是通过监视网络和服务来完成的。然而，当用户因问题呼叫客服座席时产生的用户反馈数据是实现这一目的的补充数据源。用户反馈数据特别有价值，因为它提供了用户对服务问题的看法。然而，由于用户的问题范围和护理代理使用的语言的多样性，这些数据非常嘈杂。在本文中，我们介绍LOTUS，这个系统可以根据用户反馈识别受常见根本原因（如网络中断）影响的用户。LOTUS基于一种新颖的算法框架，将协同训练和空间扫描统计紧密结合在一起。为了对用户反馈中的文本进行建模，LOTUS还结合了使用深度序列学习的定制语言模型。通过对合成数据和实时数据的实验分析，验证了LOTUS的准确性。LOTUS已经部署了几个月，并且已经确定了200多个事件的影响。
An essential step in the customer care routine of cellular service carriers is determining whether an individual user is impacted by on-going service issues. This is traditionally done by monitoring the network and the services. However, user feedback data, generated when users call customer care agents with problems, is a complementary source of data for this purpose. User feedback data is particularly valuable as it provides the user perspective of the service issues. However, this data is extremely noisy, due to range of issues that users have and the diversity of the language used by care agents. In this paper, we present LOTUS, a system that identifies users impacted by a common root cause (such as a network outage) from user feedback. LOTUS is based on novel algorithmic framework that tightly couples co-training and spatial scan statistics. To model the text in the user feedback, LOTUS also incorporates custom-built language models using deep sequence learning. Through experimental analysis on synthetic and live data, we demonstrate the accuracy of LOTUS. LOTUS has been deployed for several months, and has identified the impact over 200 events.