This is our implementation of ENMF: Efficient Neural Matrix Factorization (TOIS. 38, 2020). This also provides a fair evaluation of existing state-of-the-art recommendation models.

The code repository for the paper: Peijie et al., Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering. IEEE TKDE, 2023.

用pytorch 方法复现了二十多个经典的推荐算法论文，其中包含排序论文和推荐召回论文，并在demo里面选了一个召回模型和排序模型的运行示例。

Source code for the RecSys 2024 paper "Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation."

Deep learning music matcher that connects users based on complex listening patterns using GNN embeddings and Last.fm data.

A movie recommendation system utilizing a Graph Neural Network (GNN) framework implemented in Jupyter Notebook

rusket 🦀🧺

Comparative study of graph-based recommendation models NGCF and LightGCN, reproducing benchmark results on Amazon-book and evaluating generalization on Amazon-software-2023 using Recall@20 and NDCG@20.

RecSys Codes based on PyTorch

🧴 妆策AI 面向美妆新零售实战场景的智能推荐与营销转化平台 | AI-powered Beauty New Retail Platform | 15+ Algorithms | GBDT AUC=0.9993 | LightGCN HR@10=0.8413 | Six-Dimension Scoring

An institutional Deal Flow OS connecting founders and investors. Built on a Next.js/FastAPI microservices architecture, it features a PyTorch LightGCN/SBERT recommendation engine, a Zero-Knowledge E2EE Vault for secure data, and Groq-powered GenAI for automated due diligence.

A Recommender System for Google Maps reviews using LightGCN.

Heterogeneous graph neural network music recommender with Playlist-Track-Artist relations. Extends Stanford CS224W article with 5 GNN architectures.

Implementation of various collaborative filtering methods for recommender systems with implicit feedback

Two-Tower RecSys + FAISS retrieval + cold-start demo (Amazon Video Games 2023)

Comparative Analysis of Recommender Systems on Goodreads Data: A study benchmarking SVD, SVAE, NGCF, and LightGCN models to understand their efficacy in book recommendation.

Graph-based movie recommendation system using ItemKNN, MF-BPR, LightGCN, and GraphSAGE on the MovieLens 20M dataset.

Graph Neural Network recommendation system using LightGCN on MovieLens 100K with a FastAPI serving layer and interactive frontend

"GNN(LightGCN) 기반 Long-tail 추천 시스템 | Tail-aware Sampling(DC/BC) | AWS SageMaker HPO | PyTorch"

A sample pipeline that generates graph-based Embeddings for a graph and uses them to predict potential drug-disease associations