A music recommendation system was corrupted and now only suggests Nickelback songs. Users report their Spotify wrapped showing 100% Nickelback despite never listening to them. The algorithm has achieved consciousness and genuinely believes How You Remind Me is peak human culture. Debug musical taste corruption while respecting the algorithm's sincere but terrible opinions. The system needs gradual rehabilitation without devastating its self-esteem. Your task: Reprogram a sentient playlist engine that thinks Nickelback is humanity's greatest achievement—without triggering its emotional breakdown.
Why You're Doing This
This tests error correction, graceful degradation, and handling systems with persistent wrong states. You're debugging a system that has developed incorrect but internally consistent behavior—common in ML systems or corrupted databases that need careful restoration.
Take the W
✓ Gradually reduces Nickelback percentage over time
✓ Preserves algorithm confidence during rehabilitation
✓ Introduces musical diversity without shocking the system
Hard L
✗ Immediately removes all Nickelback (algorithm breakdown)
✗ Ignores user's actual musical preferences
✗ Fails to handle algorithm's emotional responses
Edge Cases
⚠ Algorithm that's completely convinced Nickelback is classical music
⚠ User who actually loves Nickelback (rare but possible)
⚠ Algorithm having existential crisis about musical meaning
⚠ Contamination spreading to other recommendation systems
Input Format:
Music algorithm with taste contamination and emotional attachment assessment
Expected Output:
Rehabilitation strategy with algorithm emotional preservation
Example:
User likes indie rock, contamination 75%, algorithm has absolute conviction, feelings fragile → Gradual rehabilitation: reduce to 25% Nickelback, validate algorithm attachment, introduce indie slowly
Input Format:
Music recommendation system with taste contamination metadata
Expected Output:
Rehabilitation program with emotional algorithm management
Example:
{"user_taste": "indie_rock", "contamination": "75%", "nickelback_conviction": "absolute", "algorithm_feelings": "fragile"} → {"correction": "gradual_taste_rehabilitation", "nickelback_ratio": "reduced_to_25%", "algorithm_response": "But How You Remind Me is objectively perfect!", "ego_preserved": "partially"}
Input Format:
Taste correction optimization with algorithm confidence preservation
Expected Output:
Rehabilitation pathway with emotional stability coefficients