A Snooze-less User-Aware Notification System for Proactive Conversational Agents

• URL: http://arxiv.org/abs/2003.02097v1
• Date: Wed, 4 Mar 2020 14:31:21 GMT
• Title: A Snooze-less User-Aware Notification System for Proactive Conversational Agents
• Authors: Yara Rizk, Vatche Isahagian, Merve Unuvar, Yasaman Khazaeni
• Abstract summary: We propose an alert and notification framework that intelligently issues, suppresses and aggregates notifications. Our framework can be deployed as a backend service, but is better suited to be integrated into proactive conversational agents.
• Score: 6.4378876455245235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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