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."

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

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

RecSys Codes based on PyTorch

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

A Recommender System for Google Maps reviews using LightGCN.

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

System for detecting fake news and suggesting credible alternatives. Takes a news URL and outputs a credibility score, explanation, and top reliable sources. Uses TF-IDF + Logistic Regression, XGBoost, and DistilBERT with hybrid BERT–LightGCN models, plus SHAP and GNNExplainer for interpretability.

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

Project for the exam of Semantics in Intelligent Information Access (UniBa)

This repository documents our comprehensive approach to building an effective recommendation system for predicting customer repurchases on Carrefour's eCommerce platform. Starting with simple statistical methods and progressing to advanced neural network architectures, we've explored multiple approaches to tackle this recommendation challenge.