pytorch version of neural collaborative filtering
-
Updated
Jun 7, 2024 - Jupyter Notebook
pytorch version of neural collaborative filtering
BARS: Towards Open Benchmarking for Recommender Systems https://openbenchmark.github.io/BARS
A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models
The code repository for the paper: Peijie et al., Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering. IEEE TKDE, 2023.
Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username
A Curated List of Must-read Papers on Recommender System.
I developed a simple content-based recommendation system that suggests movies to users based on their preferences. Users can enter a movie they like, and the system recommends other movies with similar genres. This project helped me understand the basics of recommendation systems and content-based filtering techniques.
Practice
An Online Book Store built in java that also recommends Books based on user's favourite book using a machine learning model in Python integrated through a Flask API.
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
[WWW'2024] "GraphPro: Graph Pre-training and Prompt Learning for Recommendation"
The official implementation of the paper "ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering"
Content-based Filtering, Neighborhood-based Collaborative Filtering
The official PyTorch implementation of the paper "RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback"
recommender system tutorial with Python
Recommendation System Based on Collaborative Filtering
A curated list of papers on cold-start recommendations.
Final project for the big data class at NYU where I developed a movie recommendation system using MovieLens database and compared its performance against the popularity based models and other vanilla metrics
Collaborative and hybrid recommendation systems
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."