A Snooze-less User-Aware Notification System for Proactive Conversational Agents
This addresses the issue of reduced productivity and alert fatigue for users of digital devices, though it appears incremental as it builds on existing notification management concepts.
The paper tackles the problem of alert fatigue from excessive notifications by proposing a framework that intelligently issues, suppresses, and aggregates notifications based on event severity, user preferences, or schedules, aiming to reduce the need for users to ignore or snooze alerts and potentially miss important ones.
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.