A dating algorithm optimizes matches for Friday night desperation levels and alcohol consumption predictions. The system factors in last-call proximity, Uber surge pricing, and emotional vulnerability scores. Matches get progressively less selective as the night progresses and loneliness metrics spike exponentially. Your task: Balance dating standards with Friday night desperation mathematics while maintaining user safety.
Why You're Doing This
You're building a recommendation system with time-based preference degradation and safety constraint management. This tests dynamic scoring algorithms, safety protocol implementation, and balancing user satisfaction with responsible outcomes. It's like surge pricing but for human emotional vulnerability and poor decision-making.
Take the W
✓ Adjusts match criteria based on time and desperation levels
✓ Implements safety controls to prevent catastrophic dating decisions
✓ Balances user satisfaction with responsible matchmaking
Hard L
✗ Completely abandons safety protocols during peak desperation
✗ Ignores time-based behavioral changes
✗ Enables clearly dangerous romantic decisions
Edge Cases
⚠ User desperation so high system recommends going home instead
⚠ All potential matches also at maximum desperation levels
⚠ Safety interventions triggering false positives
⚠ Multiple users gaming desperation metrics
Input Format:
Time of night with user desperation levels and safety requirements
Expected Output:
Adjusted matching criteria with safety interventions
Example:
11:30pm Friday, desperation level 0.8, downtown bar district, safety constraints active → 5-mile radius, standards reduced 40%, friend approval required, morning confirmation text
Input Format:
Dating context object with temporal and safety parameters