Back to cases
SpotifyProduct SenseHard~35 min
Recommendation System Improvement
Spotify's recommendation system drives 35% of all listening hours. The ML team has a new model that improves click-through rate by 8% but reduces the diversity of recommended artists by 22%. The team is debating whether to ship it. You need to evaluate tradeoffs beyond CTR, define what 'good recommendations' means for a music platform, and propose a framework for balancing exploitation (playing what users already like) with exploration (helping users discover new music).
Duration
~35 minutes
Difficulty
Hard
Free
Your first session · then $3 per session
- ~35 minute AI-powered mock interview
- Realistic interviewer pushback
- Stage-by-stage debrief with ratings
- Signal coverage map
- Reference response for comparison
Skills tested
Tradeoff evaluation beyond single-metric optimizationExploration vs. exploitation framework for recommendationsMulti-stakeholder analysis (users, artists, platform)Long-term vs. short-term metric tension
Interview stages
1
Evaluate whether the CTR improvement is truly positive
2
Design the exploration vs. exploitation framework
3
Propose how to ship the improvement responsibly
What you'll get back
After the session, you'll get a detailed debrief: stage-by-stage ratings (Strong / Developing / Weak), a signal coverage map showing which key points you hit, specific feedback on your reasoning and communication, and a reference response to compare against.
Related guide