Context

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.

Current Performance

The Problem

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.

Task

1. Diagnose why NDCG dropped from 0.85 to 0.70 while precision stayed the same.
2. Identify potential reasons for the decline in recall and user ratings (propose at least 3 hypotheses).
3. Recommend specific improvements to enhance model performance, focusing on recall and user satisfaction.
4. Discuss the implications of model changes on user engagement and retention.

Constraints

• User base: 5 million active users with diverse preferences.
• Model retraining cycle: Every quarter, with a 2-week deployment lead time.