  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.
