| --- |
| language: en |
| license: mit |
| tags: |
| - recommendation-system |
| - collaborative-filtering |
| - matrix-factorization |
| - movielens |
| - svd |
| - internship-task |
| datasets: |
| - movielens |
| model-index: |
| - name: DataSynthis_ML_JobTask |
| results: |
| - task: |
| type: recommendation |
| name: Movie Recommendation |
| dataset: |
| name: MovieLens |
| type: movielens |
| metrics: |
| - type: precision@k |
| value: 0.7460454747522295 |
| - type: recall@k |
| value: 0.5147626084794534 |
| --- |
| |
| # 🎬 Movie Recommendation System (DataSynthis ML Job Task) |
|
|
| This model was built using the MovieLens dataset for the **ML Engineer Intern task**. |
|
|
| ### Features |
| Item-based Collaborative Filtering |
| Matrix Factorization (SVD) |
| Evaluation Metrics: Precision = 0.7460454747522295, Recall = 0.5147626084794534 |
|
|
| ### How to Use |
| ```python |
| from joblib import load |
| model = load("model.joblib") |
| # Use recommend_movies(user_id, N) function |
