CineMatch is a movie recommendation system that uses collaborative filtering to suggest films to users based on their viewing history. Recently, the data science team noticed a significant drop in the system's performance metrics, particularly in user engagement and satisfaction scores. This decline has raised concerns about the effectiveness of the current model.
| Metric | Last Month | Current | Change |
|---|---|---|---|
| Precision | 0.78 | 0.78 | 0% |
| Recall | 0.65 | 0.60 | -7.7% |
| NDCG | 0.85 | 0.70 | -17.6% |
| User Ratings | 4.2 | 3.8 | -9.5% |
| Click-Through Rate (CTR) | 12% | 9% | -25% |
While precision remains stable, the drop in recall and NDCG suggests that the model is failing to retrieve relevant recommendations for users, leading to lower user ratings and engagement. The VP of Product is concerned about user retention and satisfaction.