图 书 馆
在蜂窝业务中识别受影响的用户Towards Identifying Impacted Users in Cellular Services |
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| 课程网址: | http://videolectures.net/kdd2019_venkataraman_wang_cellular/ |
| 主讲教师: | Jia Wang |
| 开课单位: | 美国电话电报公司 |
| 开课时间: | 2020-03-02 |
| 课程语种: | 英语 |
| 中文简介: |  蜂窝服务运营商的客户服务程序中的一个重要步骤是确定单个用户是否受到持续服务问题的影响。传统上，这是通过监视网络和服务来完成的。但是，当用户致电有问题的客户服务代理时生成的用户反馈数据是用于此目的的补充数据源。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。但是，由于用户存在的问题范围以及护理人员使用的语言的多样性，该数据非常嘈杂。在本文中，我们介绍了LOTUS，这是一种从用户反馈中识别受常见根本原因（例如网络中断）影响的用户的系统。 LOTUS基于新颖的算法框架，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还使用深度序列学习结合了定制的语言模型。通过对合成数据和实时数据进行实验分析，我们证明了LOTUS的准确性。 LOTUS已部署了几个月，并确定了200多个事件的影响。 |
| 课程简介: | 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 valuableas 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. |
| 关 键 词: | 蜂窝业务; 反馈数据; LOTUS; 语言模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-04-27：zhouxj |
| 最后编审: | 2020-06-01：heyf |
| 阅读次数: | 42 |