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The ubiquity of smart phones and electronic devices has placed a wealth ofinformation at the fingertips of consumers as well as creators of digitalcontent. This has led to millions of notifications being issued each secondfrom alerts about posted YouTube videos to tweets, emails and personalmessages. Adding work related notifications and we can see how quickly thenumber of notifications increases. Not only does this cause reducedproductivity and concentration but has also been shown to cause alert fatigue.This condition makes users desensitized to notifications, causing them toignore or miss important alerts. Depending on what domain users work in, thecost of missing a notification can vary from a mere inconvenience to life anddeath. Therefore, in this work, we propose an alert and notification frameworkthat intelligently issues, suppresses and aggregates notifications, based onevent severity, user preferences, or schedules, to minimize the need for usersto ignore, or snooze their notifications and potentially forget aboutaddressing important ones. Our framework can be deployed as a backend service,but is better suited to be integrated into proactive conversational agents, afield receiving a lot of attention with the digital transformation era, emailservices, news services and others. However, the main challenge lies indeveloping the right machine learning algorithms that can learn models from awide set of users while customizing these models to individual users'preferences.

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