一个面向主动会话代理的无休眠用户感知通知系统
A Snooze-less User-Aware Notification System for Proactive Conversational Agents
摘要：
智能手机和电子设备的普及已经将大量信息置于消费者以及数字内容的创建者的指尖。这导致每秒发出数百万条通知，从发布YouTube视频的警报到推文、电子邮件和个人消息。添加与工作相关的通知，我们可以看到通知的数量增加得有多快。这不仅会导致生产力和注意力下降，而且还会导致警觉疲劳。这种情况使用户对通知不敏感，导致他们忽略或错过重要警报。根据用户工作的领域，错过通知的成本可能从仅仅是不便到生死攸关。因此，在这项工作中，我们提出了一个警报和通知框架，智能问题，抑制和聚合通知，根据事件的严重性，用户的偏好，或时间表，以尽量减少用户忽略，或打盹他们的通知，并可能忘记解决重要的。我们的框架可以部署为后端服务，但更适合集成到主动会话代理中，这是一个在数字化转型时代受到广泛关注的领域，电子邮件服务，新闻服务等。然而，主要的挑战在于开发正确的机器学习算法，可以从广泛的用户集合中学习模型，同时根据个人用户的偏好定制这些模型。
英文摘要：
The ubiquity of smart phones and electronic devices has placed a wealth of information at the fingertips of consumers as well as creators of digital content. This has led to millions of notifications being issued each second from alerts about posted YouTube videos to tweets, emails and personal messages. Adding work related notifications and we can see how quickly the number of notifications increases. Not only does this cause reduced productivity and concentration but has also been shown to cause alert fatigue. This condition makes users desensitized to notifications, causing them to ignore or miss important alerts. Depending on what domain users work in, the cost of missing a notification can vary from a mere inconvenience to life and death. Therefore, in this work, we propose an alert and notification framework that intelligently issues, suppresses and aggregates notifications, based on event severity, user preferences, or schedules, to minimize the need for users to ignore, or snooze their notifications and potentially forget about addressing important ones. Our framework can be deployed as a backend service, but is better suited to be integrated into proactive conversational agents, a field receiving a lot of attention with the digital transformation era, email services, news services and others. However, the main challenge lies in developing the right machine learning algorithms that can learn models from a wide set of users while customizing these models to individual users' preferences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
