| | --- |
| | 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@K, Recall@K, NDCG |
| |
|
| | ### How to Use |
| | ```python |
| | from joblib import load |
| | model = load("model.joblib") |
| | # Use recommend_movies(user_id, N) function |
