| { | |
| "model_type": "movie_recommendation", | |
| "name": "DataSynthis_ML_JobTask", | |
| "description": "Movie recommendation system using collaborative filtering and matrix factorization", | |
| "version": "1.0.0", | |
| "author": "tasdid25", | |
| "license": "MIT", | |
| "framework": "scikit-learn", | |
| "algorithms": [ | |
| "collaborative_filtering", | |
| "matrix_factorization_svd" | |
| ], | |
| "dataset": "movielens_100k", | |
| "features": { | |
| "user_id_range": [1, 943], | |
| "movie_count": 1682, | |
| "rating_count": 100000, | |
| "recommendation_methods": ["svd", "cf"], | |
| "max_recommendations": 20 | |
| }, | |
| "input_schema": { | |
| "user_id": { | |
| "type": "integer", | |
| "description": "User ID (1-943)", | |
| "required": true | |
| }, | |
| "n_recommendations": { | |
| "type": "integer", | |
| "description": "Number of recommendations (1-20)", | |
| "default": 10, | |
| "required": false | |
| }, | |
| "method": { | |
| "type": "string", | |
| "description": "Recommendation method", | |
| "enum": ["svd", "cf"], | |
| "default": "svd", | |
| "required": false | |
| } | |
| }, | |
| "output_schema": { | |
| "type": "array", | |
| "items": { | |
| "type": "object", | |
| "properties": { | |
| "movie_id": { | |
| "type": "integer", | |
| "description": "Movie ID" | |
| }, | |
| "title": { | |
| "type": "string", | |
| "description": "Movie title" | |
| }, | |
| "predicted_rating": { | |
| "type": "number", | |
| "description": "Predicted rating for the user" | |
| } | |
| } | |
| } | |
| }, | |
| "dependencies": [ | |
| "pandas>=2.0.0", | |
| "numpy>=1.24.0", | |
| "scikit-learn>=1.3.0" | |
| ], | |
| "inference_function": "predict" | |
| } |