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Movie Recommendation System Models
Overview
This repository contains serialized model artifacts used by the Movie Recommendation System project.
The recommendation engine is a Content-Based Recommendation System built using Natural Language Processing techniques.
GitHub Repository:
Model Details
Technique
- TF-IDF Vectorization
- Cosine Similarity
Recommendation Type
Content-Based Filtering
Input
Movie title
Output
Top-N similar movies ranked by similarity score.
Files
movies.pkl
Processed movie metadata used by the recommendation engine.
similarity.pkl
Precomputed cosine similarity matrix used for fast recommendation retrieval.
Dataset
TMDB 5000 Movie Dataset
Source: https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata
Usage
Place the downloaded files inside:
models/
├── movies.pkl
└── similarity.pkl
Then load them in Python:
import pickle
movies = pickle.load(open("models/movies.pkl", "rb"))
similarity = pickle.load(open("models/similarity.pkl", "rb"))
Limitations
Current version uses movie overview text as the primary feature source.
Recommendations can be improved by incorporating:
- Genres
- Keywords
- Cast Information
- Director Information
- User Ratings
Intended Use
Educational and portfolio demonstration project showcasing:
- NLP
- Recommendation Systems
- TF-IDF Vectorization
- Similarity Search
- Machine Learning Workflows