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).

Skills tested

Interview stages

• 1Evaluate whether the CTR improvement is truly positive
• 2Design the exploration vs. exploitation framework
• 3Propose how to ship the improvement responsibly

What you'll get back

After the session, you'll receive a detailed AI-generated debrief with 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 for comparison.