Iteration 2 Summary
Business Context: A movie streaming platform aims to maximize viewer satisfaction by recommending movies based on ratings. The platform needs to allocate a limited number of recommendations to movies such that the total satisfaction (sum of stars) is maximized, while ensuring that no movie is recommended more than a certain number of times and no reviewer is overloaded with recommendations.
Optimization Problem: The platform needs to decide how many times each movie should be recommended to maximize the total satisfaction (sum of stars) from the ratings. Constraints include limiting the number of recommendations per movie and ensuring that no reviewer receives too many recommendations.
Objective: maximize ∑(stars[i,j] * x[i,j]) where x[i,j] is the number of times movie j is recommended to reviewer i
Tables Created: 1
Tables Modified: 0
Tables Deleted: 0
Key Change: Added a new table for star ratings to address missing objective coefficients, updated business configuration logic, and ensured all mappings are complete.
Status: Complete
Confidence: high
Next Focus: Ready for convergence
Mapping Adequacy: mostly_good
Business Configuration Parameters: 0

Triple Expert Data: Values were determined based on realistic business scenarios, ensuring that the optimization problem remains meaningful and solvable. Constraints were respected, and relationships between tables were maintained to ensure consistency.