collaborative filtering, content-based filtering, and hybrid recommendation techniques
Movie recommendation systems are widely used in streaming platforms like Netflix, Amazon Prime, and Disney+ to suggest movies based on user preferences. This project involves developing a system that suggests movies based on either collaborative filtering (user behavior), content-based filtering (movie attributes), or a hybrid approach. The system can be enhanced with deep learning models like neural collaborative filtering to improve accuracy.
Programming Languages: Python, R Libraries & Frameworks: Pandas, NumPy, Scikit-learn, TensorFlow/Keras, Surprise, LightFM, NLTK (for content-based filtering) Databases: PostgreSQL, MySQL, MongoDB (for storing user-movie interactions) Tools & Platforms: Jupyter Notebook, Google Colab, AWS/Azure (for cloud-based model deployment), Streamlit/Flask (for UI development) Before Commencing the project the following links have to be examined.
https://www.kaggle.com/
https://developer.imdb.com/non-commercial-datasets/
https://datasetsearch.research.google.com/
https://grouplens.org/datasets/movielens/
