图 书 馆
识别蜂窝服务中受影响的用户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: |  |
| 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: | 数据科学; 识别蜂窝服务; 受影响的用户; 蜂窝服务运营商客户服务 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-09-15：cyh |
| 最后编审: | 2022-09-19：cyh |
| 阅读次数: | 30 |