Iteration 1 Summary
Business Context: A movie streaming platform wants to maximize viewer satisfaction by recommending movies based on ratings. The platform aims 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: 3
Tables Modified: 0
Tables Deleted: 0
Key Change: Schema changes include creating tables for missing constraint bounds and decision variables, and updating business configuration logic for scalar parameters. Data dictionary and mapping are updated to reflect these changes.
Status: In progress
Confidence: medium
Next Focus: Define and map the missing constraint bounds for max_recommendations_per_movie and max_recommendations_per_reviewer
Mapping Adequacy: needs_improvement
Business Configuration Parameters: 2
