Reinforcement Learning

As a potential solution for notification management, reinforcement learning methods are tested on existing in-the-wild and subsequent synthetic data sets.

There are many features which are known to have an association with the user's action on a notification, however the number of features selected also expands the state-space and impacts training/prediction times. For the purposes of this preliminary study, only the following features (which are known at delivery time) were selected:
{ category, app, time-of-day, day-of-week }

Q-Table

A Q-Table is implemented to learn to mediate notification delivery on behalf of the user to aleviate information overload.

Example Result:
Trained on 3,866 notifications, tested on 429 using 10-Fold Cross-Validation
Accuracy: 78%
Precision: 78%
Recall: 81%
F1: 80%
Time to Train: 70s
Time to Test: 39s

Deep Q-Network

A Deep Q-Network (DQN) is implemented to learn to mediate notification delivery on behalf of the user to aleviate information overload.

Example Result:
Trained on 3,866 notifications, tested on 429 using 10-Fold Cross-Validation
Accuracy: 80%
Precision: 79%
Recall: 87%
F1: 83%
Time to Train: 257s
Time to Test: 19s