In this project, I analyze the interactions that users have with articles on the IBM Watson Studio platform, and recommend new articles.
Book Recommendation System A recommendation system for suggesting new books given similar books. Datasets Dataset Kaggle Dataset Notebooks goodreads-E
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Basic Information: This code is released for the papers: Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang and Meng Wang. A Neural Influence Dif
SR-HGNN ICDM-2020 《Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks》 Environments python 3.8 pytorch-1.6 DGL 0.5.
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation This repository contains the source code of the SIGIR 2019 paper "Reinforcement
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
🔍 E-Commerce recommender demo 🔍 This is a simple stream setup that uses Memgraph to ingest real-time data from a simulated online store. Data is str
Content-Based-Movie-Recommender-System This model relies on the similarity of the items being recommended. (I have used Pandas and Numpy. However othe
PPGN Codes for CIKM 2019 paper Cross-Domain Recommendation via Preference Propagation GraphNet. Citation Please cite our paper if you find this code u
Movie Recommendation 🍿 System Watch Tutorial for this project Source IMDB Movie 5000 Dataset Inspired from this original repository. Features Simple
KHGT Code for KHGT accepted by AAAI2021 Please unzip the data files in Datasets/ first. To run KHGT on Yelp data, use python labcode_yelp.py For Movi
Overview Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data. Surprise was designed with th
Movie-Recommender-System Movie-Recommender-System is a web application using which a user can select his/her watched movie from list and system will r
FairGNN A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (
Books-Recommendation Business Problem During the last few decades, with the rise
RecSim NG, a probabilistic platform for multi-agent recommender systems simulation. RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a power
Recommendation engines are one of the most well known, widely used and highest value use cases for applying machine learning. Despite this, while there are many resources available for the basics of
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation Pytorch based implemention of Relational Temporal
This repo contains a tensorflow implementation of RecoGCN and the experiment dataset Running the RecoGCN model python train.py Example training outp
Detecting Beneficial Feature Interactions for Recommender Systems (L0-SIGN) This is our implementation for the paper: Su, Y., Zhang, R., Erfani, S., &
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