Which algorithm is BEST suited for determining the optimal frequency and timing of push notifications on Live.com to maximize user engagement without causing annoyance?

A Multi-Armed Bandit (MAB) algorithm is BEST suited for determining the optimal frequency and timing of push notifications on Live.com to maximize user engagement without causing annoyance. A Multi-Armed Bandit algorithm is a reinforcement learning technique that addresses the exploration-exploitation dilemma. In the context of push notifications, 'exploration' involves trying different notification frequencies and timings to see which performs best, while 'exploitation' involves leveraging the knowledge gained from exploration to send notifications at the most effective times. The MAB algorithm continuously learns from user responses (e.g., notification opens, clicks, dismissals) to adjust the notification strategy in real-time. It balances the need to explore new possibilities with the need to exploit existing knowledge. For example, if the algorithm discovers that users are more likely to engage with notifications sent in the evening, it will gradually increase the frequency of notifications sent during that time, while still occasionally exploring other times to ensure it's not missing out on potentially better opportunities. This adaptive approach allows Live.com to personalize the notification strategy for each user, maximizing engagement while minimizing the risk of annoyance and opt-outs.