Practice
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Updated
Jun 10, 2024 - Jupyter Notebook
Practice
[SIGIR'2023] "GFormer: Graph Transformer for Recommendation"
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
[WWW'2024] "GraphPro: Graph Pre-training and Prompt Learning for Recommendation"
Collaborative and hybrid recommendation systems
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
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.
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.
The official implementation of the paper "ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering"
Content-based Filtering, Neighborhood-based Collaborative Filtering
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