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

A simple Product Recommendation System.

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

Collaborative and hybrid recommendation systems

USC DSCI 553 - Foundations & Applications of Data Mining - Spring 2024 - Prof. Wei-Min Shen

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.

A Comparative Framework for Multimodal Recommender Systems

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"