Movie Recommendation System

Movie recommendation system with Python. Implements content-based filtering (TF-IDF + cosine similarity), collaborative filtering with matrix factorization (TruncatedSVD), and a hybrid approach. Evaluates with Precision@K, Recall@K, and NDCG. Includes rating distribution plots, top movies, and sample recommendations.

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Features

• Synthetic ratings dataset with users, movies, and multi-label genres
• Content-based: TF‑IDF on genres + item–item cosine similarity
• Collaborative: user–item matrix factorization (TruncatedSVD)
• Hybrid: weighted combination of content & collaborative scores
• Evaluation: Precision@K, Recall@K, NDCG@K (per-user & macro)
• Visuals: rating distribution, top movies, sample recommendations
• Outputs: metrics JSON, recommendation files, and charts

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Project Structure

movie-recommendation-system/
├─ README.md
├─ LICENSE
├─ requirements.txt
├─ data/
│  └─ generate_ratings.py
├─ src/
│  ├─ build_recommender.py
│  └─ utils.py
└─ outputs/
   └─ figures & reports (auto-created)

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Setup

python -m venv .venv
# Windows:
.venv\Scripts\activate
# macOS/Linux:
source .venv/bin/activate
pip install -r requirements.txt

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Generate Synthetic Dataset

python data/generate_ratings.py --users 800 --movies 1200 --seed 42 --outdir data
# Produces: data/ratings.csv, data/movies.csv

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Train, Evaluate & Recommend

python src/build_recommender.py --ratings data/ratings.csv --movies data/movies.csv --outdir outputs --k 10 --alpha 0.6 --seed 42

Outputs

• outputs/metrics.json – Precision@K, Recall@K, NDCG@K (content, collab, hybrid)
• outputs/recs_user_*.txt – sample recommendations for a few users
• outputs/ratings_hist.png
• outputs/top_movies.png

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Example Results (K=10, α=0.6)

Evaluation Metrics

Method	Precision@10	Recall@10	NDCG@10
Collaborative	0.0024	0.0107	0.0059
Content-based	0.0026	0.0129	0.0073
Hybrid (α=0.6)	0.0024	0.0107	0.0060

➡️ On this sparse synthetic dataset, content-based slightly outperforms collaborative and hybrid.

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Ratings Distribution

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Top Movies by Avg Rating

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Sample Recommendations

User 1

Movie 959    41.08
Movie 541    36.28
Movie 952    34.83
Movie 95     34.81
Movie 40     33.30

User 2

Movie 915    47.02
Movie 122    38.27
Movie 77     38.18
Movie 1097   37.65
Movie 952    37.04

User 3

Movie 800    52.00
Movie 410    45.77
Movie 9      45.59
Movie 738    41.34
Movie 713    41.00

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Notes

• Positive items are defined as ratings ≥ 4 for evaluation.
• The hybrid weight alpha controls emphasis on collaborative (α→1) vs. content (α→0).