A movie recommendation system using IMDb's weighted ratings and custom filters.
-
Updated
May 31, 2024 - Jupyter Notebook
A movie recommendation system using IMDb's weighted ratings and custom filters.
This movie recommendation system employs content-based, collaborative, and popularity-based filtering techniques, using Cosine Similarity, to provide personalized movie suggestions. By combining diverse algorithms, the system enhances user experience by offering a well-rounded selection of films tailored to individual preferences.
The Book Recommendation System is designed to provide personalized book suggestions to users based on their preferences and past interactions. Using popular-based filtering and collaborative filtering, the system helps users discover books they may enjoy. The project follows a modular coding approach, making it scalable and maintainable.
Popularity Based & Collaborative Filtering based Recommender System.
A complete Movie Recommendation System project implementing Popularity-Based, Content-Based, and Collaborative Filtering models using the MovieLens dataset. Built with Python, Pandas, and Plotly, featuring interactive inputs and visualizations.
Add a description, image, and links to the popularity-based-filtering topic page so that developers can more easily learn about it.
To associate your repository with the popularity-based-filtering topic, visit your repo's landing page and select "manage topics."