Content-based Filtering, Neighborhood-based Collaborative Filtering

A book recommendation system using machine learning is designed to provide personalized reading suggestions to users based on their preferences, reading history, and behavior. The system employs various machine learning algorithms and techniques to analyze large datasets and predict the likelihood of a user enjoying a particular book.

Gorse open source recommender system engine

Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username

PyTorch Implementation of Context-Aware Sequential Model for Multi-Behaviour Recommendation https://arxiv.org/abs/2312.09684

Versatile End-to-End Recommender System

A unified, comprehensive and efficient recommendation library

Collaborative and hybrid recommendation systems

Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)

Neural collaborative filtering recommendation system on Movie lens 100k dataset

Movie Recommender

Product Recommender

A Comparative Framework for Multimodal Recommender Systems

Create recommender systems testing various algorithms

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.