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智能手机实时设备故障排除建议
Real-time On-Device Troubleshooting Recommendation for Smartphones |
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| 课程网址: |
http://videolectures.net/kdd2019_ochiai_senkawa_yamamoto/
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| 主讲教师: |
Keiichi Ochiai
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| 开课单位: |
NTT DOCOMO, Inc.
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| 开课时间: |
2020-03-02
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| 课程语种: |
英语
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| 中文简介: |
每天都有数十亿人在使用智能手机，他们经常面临硬件和软件方面的问题和麻烦。这些问题会导致用户沮丧，客户满意度低。开发基于自动机器学习的解决方案，以检测用户有问题并进行故障排除，有可能显著提高客户满意度和保留率。在这里，我们设计并实现了一个基于用户智能手机活动检测用户有问题并需要帮助的系统。我们的系统会自动检测到用户有问题，然后通过推荐可能的解决方案来帮助进行故障排除。我们基于大规模客户支持中心数据对系统进行培训，结果表明，它既可以检测到用户有问题，也可以预测问题的类别（准确率为89.7%），并快速提供解决方案（10.4ms）。自2019年1月以来，我们的系统已部署到商业服务中。在线评估结果表明，基于机器学习的方法在用户问题解决率方面比现有方法优越约30%。
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| 课程简介: |
Billions of people are using smartphones everyday and they often face problems and troubles with both the hardware as well as the software. Such problems lead to frustrated users and low customer satisfaction. Developing an automatic machine learning-based solution that would detect that the user has a problem and would engage in troubleshooting has the potential to significantly improve customer satisfaction and retention. Here, we design and implement a system that based on the user’s smartphone activity detects that the user has a problem and requires help. Our system automatically detects a user has a problem and then helps with the troubleshooting by recommending possible solutions to the identified problem. We train our system based on large-scale customer support center data and show that it can both detect that a user has a problem as well as predict the category of the problem (89.7% accuracy) and quickly provide a solution (in 10.4ms). Our system has been deployed in commercial service since January, 2019. Online evaluation result showed that machine learning based approach outperforms the existing method by approximately 30% regarding the user problem solving rate.
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| 关 键 词: |
智能手机实时设备; 手机实时设备故障; 故障排除建议
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| 课程来源: |
视频讲座网
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| 数据采集: |
2022-09-19：cyh
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| 最后编审: |
2022-09-19：cyh
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| 阅读次数: |
123
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