Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)
-
Updated
May 29, 2024 - Jupyter Notebook
Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)
Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username
Neural collaborative filtering recommendation system on Movie lens 100k dataset
Movie Recommender
Product Recommender
Collaborative and hybrid recommendation systems
Content-based Filtering, Neighborhood-based Collaborative Filtering
A Comparative Framework for Multimodal Recommender Systems
Gorse open source recommender system engine
Create recommender systems testing various algorithms
Versatile End-to-End Recommender System
We are proud to introduce our new book recommendation system, book.io. This system uses the user-to-user collaborative filtering model to recommend books to users based on their preferences and ratings.
A recommender system built from scratch using the collaboration filtering algorithm and NumPy library
This project developed two wine recommendation models using the XWines dataset, employing collaborative filtering and content-based techniques. It leveraged Python, Numpy, Pandas, Jupyter Notebook, VSCode, and Scikit-learn.
Code associated with "Benchmarking collaborative filtering approaches to drug repurposing"
This repository contains a recommendation system implemented using the Apriori algorithm for frequent itemset mining and association rule generation. The recommendation system aims to suggest relevant products to users based on their past purchase history.
Movie Recommendation System using Collaborative Method (User - User similarity , Item-Item similarity)
Add a description, image, and links to the collaborative-filtering topic page so that developers can more easily learn about it.
To associate your repository with the collaborative-filtering topic, visit your repo's landing page and select "manage topics."