# Model Card: Recommender

## Model Details

- **Model Name:** recommender
- **Model Version:** recommender_v2_baseline_001
- **Algorithm:** GradientBoostingClassifier (two models: clicked, is_completed)
- **Framework:** scikit-learn
- **Trained At:** 2026-05-21T05:59:13.148420+00:00
- **Seed:** 42

## Intended Use

Predict whether a student will click on a recommendation and whether they will
complete the recommended content. Used in the recommendation engine to rank
content by predicted engagement. Two separate binary classifiers are trained:
one for `clicked` and one for `is_completed`.

## Training Data

- **Source:** training_recommendation_outcomes.csv (synthetic dataset v2)
- **Split Counts:** train=5671, validation=1214, test=1215
- **Features:** priority (OrdinalEncoded), ai_confidence (numeric), recommendation_type (OrdinalEncoded), grade (numeric), subject (OrdinalEncoded)
- **Targets:** clicked (binary), is_completed (binary)

## Metrics

### Validation Set
- ROC-AUC (clicked): 0.5439
- ROC-AUC (is_completed): 0.5303
- Lift@10 (clicked): 1.1392
- Lift@10 (is_completed): 1.1056

### Test Set
- ROC-AUC (clicked): 0.5486
- ROC-AUC (is_completed): 0.5424
- Lift@10 (clicked): 1.0471
- Lift@10 (is_completed): 1.0366

## Known Limitations

- Trained on synthetic data only — performance on real recommendation data is unknown.
- Two separate GBC models — no joint optimization of clicked + is_completed.
- OrdinalEncoder assumes an ordering for priority/recommendation_type/subject.
- Lift@10 depends on the distribution of positive labels in the dataset.
- No user-level features (e.g., engagement history) included in baseline.
- Limited feature set (5 features); adding student history could improve performance.

## Fallback Behavior

When the model is not loaded or confidence is below threshold, the system
falls back to knowledge-graph weakest-prerequisite + content_catalog filtered
by LO + difficulty, ranked by estimated_mastery_gain.