Joint Embedding-classifier Learning for improved Interpretability (JELI)

Versatile End-to-End Recommender System

Wanderlust Reads is a Flask-driven book application that uses Wikipedia API for book searching and techniques like Collaborative Filtering using the KNN model for recommendations. It uses HTML, CSS, and Vanilla JavaScript for the front end. It also has an add-to-cart and ordering books functionality.

Collaborative and hybrid recommendation systems

A recommender system built from scratch based on collaboration filtering algorithm in Python using NumPy library

[WWW '23] The official implmentation of our paper "Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation"

[SIGIR '23] The official implementation of paper "Collaborative Residual Metric Learning"

A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models

(Python, R, C) Collective (multi-view/multi-way) matrix factorization, including cold-start functionality (recommender systems, imputation, dimensionality reduction)

A Comparative Framework for Multimodal Recommender Systems

This is an app for collaborative and hybrid filtering using multiple csv data a model is trained and a flask is used for the web representation of model

Project based on Collaborative Filtering Recommendation System (User-Based) using Streamlit on movies dataset

Project based on Collaborative Filtering Recommendation System (Item-based) using Streamlit on movies dataset

Project based on Collaborative filtering using KNN clustering on books dataset, along with Streamlit webapp

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

Movie Recommendation System based on the Spearman's rank correlation 🎞️

This repository offers a comprehensive suite of models for building a robust movie recommendation system. It explores various recommendation techniques including collaborative filtering, content-based filtering, and matrix factorization. Each approach is designed to enhance the user experience by providing personalized movie suggestions. Detailed d

Lhydra Hybrid Music Recommender System

Recommendation System for Books using Collaborative Filterings: An ML Project to Recommend 'n' similar Books for a given book, as per the Collaborative users' ratings of the books. This Project also involves the deployment in a Flask Based web application.

BARS: Towards Open Benchmarking for Recommender Systems https://openbenchmark.github.io/BARS