Notification/Push Notification System
Notifications drive re-engagement but quickly become spam if over-sent, so the central challenge is optimizing which notifications to send, to whom, and when, in a way that maximizes user value rather than just open rates. I'll work through business and ML objectives, system architecture, data and features, modeling, infrastructure, evaluation, and robustness.
Solution Walkthrough
Business Objective
The objective is to maximize long-term user engagement and satisfaction by sending timely, relevant notifications while respecting user attention and preventing notification fatigue. We're optimizing for sustained platform value, not just short-term click-through rates. A notification that gets clicked but annoys the user is worse than no notification.
The core tradeoff is engagement vs fatigue. Each notification has potential value (bringing user back to platform, alerting them to important updates) but also cost (interrupting them, training them to ignore notifications, potentially leading to opt-out). We need to send notifications that are worth the interruption.
Different notification types have different value profiles: high-value notifications (friend tagged you in photo, someone commented on your post, messages from close friends) justify interruption, medium-value notifications (friend posted something, event reminder, group activity) might be batched or shown only if user likely to engage, and low-value notifications (generic engagement bait, distant connections' activity) probably shouldn't be sent at all.
The system must handle multiple objectives simultaneously: bringing users back to the platform, driving engagement with specific features, supporting creator ecosystem (ensuring creators' content reaches audiences), maintaining notification channel health (avoiding spam, respecting preferences), and balancing global optimization (what's best for platform) with individual utility (what's actually useful to this user).
ML Objective
From an ML perspective, this is multi-objective optimization with constraints. For each potential notification and user, we need to predict: probability user will engage if sent (click, open app), expected value of engagement (will they just dismiss or actually interact meaningfully?), probability of negative action (dismiss, turn off notifications, uninstall app), and optimal send time (when is user most receptive?).
These predictions feed into a constrained optimization: maximize expected engagement value, subject to: maximum N notifications per user per day (frequency cap), minimum quality threshold (don't send low-value notifications), user preference constraints (respect notification settings, quiet hours), and fairness constraints (don't over-notify certain user segments).
The system must also solve send-time optimization: given a notification we want to send, when should we send it? Immediately? Wait until evening when user is typically active? Batch with other notifications? This requires modeling user availability patterns and receptiveness windows.
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