Collaborative Filtering (CF): Item-based similarity using cosine similarity
Matrix Factorization (SVD): Singular Value Decomposition for dimensionality reduction

Dataset

MovieLens 100k: 100,000 ratings from 943 users on 1,682 movies
User ID Range: 1-943
Movie Count: 1,682 unique movies
Rating Scale: 1-5 stars

Usage

Python

python

from model import predict

# Get recommendations using SVD (default)
recommendations = predict(user_id=1, n_recommendations=10, method="svd")

# Get recommendations using collaborative filtering
recommendations = predict(user_id=1, n_recommendations=10, method="cf")

print(recommendations)

Parameters

user_id (int): User ID between 1-943 (required)
n_recommendations (int): Number of recommendations between 1-20 (default: 10)
method (str): "svd" for matrix factorization or "cf" for collaborative filtering (default: "svd")

Output

Returns a list of dictionaries with movie recommendations:

json

[
  {
    "movie_id": 50,
    "title": "Star Wars (1977)",
    "predicted_rating": 4.5
  },
  {
    "movie_id": 181,
    "title": "Return of the Jedi (1983)",
    "predicted_rating": 4.3
  }
]

Model Performance

SVD Method: Fast predictions with good accuracy using 20 components
Collaborative Filtering: More interpretable, based on item similarity
Cold Start Handling: Graceful error handling for unknown users

Technical Details

Framework: Scikit-learn
Algorithms: TruncatedSVD, Cosine Similarity
Data Processing: Pandas for efficient matrix operations
Memory Efficient: Optimized for large-scale recommendation tasks

Installation

pip install pandas numpy scikit-learn

Training

The model is pre-trained on the MovieLens 100k dataset. To retrain:

python

from model import MovieRecommender

model = MovieRecommender()
model.load_data()
model.train()
model.save_model("movie_recommender.pkl")

Citation

bibtex

@misc{datasynthis_ml_jobtask,
  title={DataSynthis ML JobTask: Movie Recommendation System},
  author={tasdid25},
  year={2025},
  url={https://huggingface.co/tasdid25/DataSynthis_ML_JobTask}
}

License

MIT License - see LICENSE file for details.