Design an Adaptive UI for a Streaming-Platform Recommender
Overview
What this challenge is about.
Design 3 adaptive layout variants triggered by session-intent signals (time of day, last-3-session pattern, device class) — without retraining the recommender. Build a high-fidelity React prototype (mock data) with state machine for intent transitions. Run a 4-week server-side A/B test against the static-grid baseline, segmented by intent class. Analyze click-through, time-to-first-play, and 7-day retention. Deliver: interaction spec, 3 layout variants, prototype repo, A/B-test report, and a roadmap for which variant to ship.
The Brief
What you'll do, and what you'll demonstrate.
Lift click-through from 4.2 percent to 6 percent via an adaptive UI layer over the existing recommender, validated by a 4-week A/B test.
Earning criteria — what you'll demonstrate
- Design adaptive interfaces that respond to session-intent signals
- Apply HCI research methods to intent inference without violating privacy norms
- Run a statistically valid A/B test for an adaptive UI
- Translate test results into a shippable product recommendation
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Product Manager
Product managers who can co-own an adaptive-UI experiment from design to A/B test results earn engineering's trust on the next risky bet.
This challenge sharpens
- a-b-testing
- behavior-change-design
- interaction-design