| | ---
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| | license: mit
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| | tags:
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| | - recommendation-system
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| | - collaborative-filtering
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| | - matrix-factorization
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| | - movie-recommendations
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| | - movielens
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| | - machine-learning
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| | library_name: scikit-learn
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| | ---
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| |
|
| | # DataSynthis_ML_JobTask
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| |
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| | A powerful movie recommendation system using collaborative filtering and matrix factorization techniques on the MovieLens 100k dataset.
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| |
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| | ## Model Description
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| |
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| | This model provides personalized movie recommendations using two state-of-the-art algorithms:
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| |
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| | - **Collaborative Filtering (CF)**: Item-based similarity using cosine similarity
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| | - **Matrix Factorization (SVD)**: Singular Value Decomposition for dimensionality reduction
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| |
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| | ## Dataset
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| |
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| | - **MovieLens 100k**: 100,000 ratings from 943 users on 1,682 movies
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| | - **User ID Range**: 1-943
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| | - **Movie Count**: 1,682 unique movies
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| | - **Rating Scale**: 1-5 stars
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| |
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| | ## Usage
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| |
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| | ### Python
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| |
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| | ```python
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| | from model import predict
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| |
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| | # Get recommendations using SVD (default)
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| | recommendations = predict(user_id=1, n_recommendations=10, method="svd")
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| |
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| | # Get recommendations using collaborative filtering
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| | recommendations = predict(user_id=1, n_recommendations=10, method="cf")
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| |
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| | print(recommendations)
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| | ```
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| |
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| | ### Parameters
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| |
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| | - **user_id** (int): User ID between 1-943 (required)
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| | - **n_recommendations** (int): Number of recommendations between 1-20 (default: 10)
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| | - **method** (str): "svd" for matrix factorization or "cf" for collaborative filtering (default: "svd")
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| |
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| | ### Output
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| |
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| | Returns a list of dictionaries with movie recommendations:
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| |
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| | ```json
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| | [
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| | {
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| | "movie_id": 50,
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| | "title": "Star Wars (1977)",
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| | "predicted_rating": 4.5
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| | },
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| | {
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| | "movie_id": 181,
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| | "title": "Return of the Jedi (1983)",
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| | "predicted_rating": 4.3
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| | }
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| | ]
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| | ```
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| |
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| | ## Model Performance
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| |
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| | - **SVD Method**: Fast predictions with good accuracy using 20 components
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| | - **Collaborative Filtering**: More interpretable, based on item similarity
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| | - **Cold Start Handling**: Graceful error handling for unknown users
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| |
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| | ## Technical Details
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| |
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| | - **Framework**: Scikit-learn
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| | - **Algorithms**: TruncatedSVD, Cosine Similarity
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| | - **Data Processing**: Pandas for efficient matrix operations
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| | - **Memory Efficient**: Optimized for large-scale recommendation tasks
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| |
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| | ## Installation
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| |
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| | ```bash
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| | pip install pandas numpy scikit-learn
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| | ```
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| |
|
| | ## Training
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| |
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| | The model is pre-trained on the MovieLens 100k dataset. To retrain:
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| |
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| | ```python
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| | from model import MovieRecommender
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| |
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| | model = MovieRecommender()
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| | model.load_data()
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| | model.train()
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| | model.save_model("movie_recommender.pkl")
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| | ```
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| |
|
| | ## Citation
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| |
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| | ```bibtex
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| | @misc{datasynthis_ml_jobtask,
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| | title={DataSynthis ML JobTask: Movie Recommendation System},
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| | author={tasdid25},
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| | year={2025},
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| | url={https://huggingface.co/tasdid25/DataSynthis_ML_JobTask}
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| | }
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| | ```
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| |
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| | ## License
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| |
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| | MIT License - see LICENSE file for details. |
