Crab - A Python Library for Recommendation Engines This library intends to be a reference for recommendation engines in Python Programming language. It is written in pure python to maximize the cross-platform issue and exposes the recommendation logic to your application by easy to use API REST via web services. The library is extensible, so the user can create new representations, algorithms and the design is optimized for performance. It is also open-source so everyone can use it. If you want to see our plan release/roadmap, please take a look at our Issues Tracker: http://github.com/marcelcaraciolo/crab/issues
This library intends to be a reference for recommendation engines in Python
Overview
The source code for "Global Context Enhanced Graph Neural Network for Session-based Recommendation".
GCE-GNN Code This is the source code for SIGIR 2020 Paper: Global Context Enhanced Graph Neural Networks for Session-based Recommendation. Requirement
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
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
A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (WSDM 2021)
FairGNN A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (
Price-aware Recommendation with Graph Convolutional Networks,
PUP This is the official implementation of our ICDE'20 paper: Yu Zheng, Chen Gao, Xiangnan He, Yong Li, Depeng Jin, Price-aware Recommendation with Gr
EXEMPLO DE SISTEMA ESPECIALISTA PARA RECOMENDAR SERIADOS EM PYTHON
exemplo-de-sistema-especialista EXEMPLO DE SISTEMA ESPECIALISTA PARA RECOMENDAR SERIADOS EM PYTHON Resumo O objetivo de auxiliar o usuário na escolha
fastFM: A Library for Factorization Machines
Citing fastFM The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citat
Spark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset
A scalable on-line movie recommender using Spark and Flask This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens datase
Learning Fair Representations for Recommendation: A Graph-based Perspective, WWW2021
FairGo WWW2021 Learning Fair Representations for Recommendation: A Graph-based Perspective As a key application of artificial intelligence, recommende
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Annoy Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given quer
Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks
Bi-TGCF Tensorflow Implementation of BiTGCF: Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks. in CIKM20
Collaborative variational bandwidth auto-encoder (VBAE) for recommender systems.
Collaborative Variational Bandwidth Auto-encoder The codes are associated with the following paper: Collaborative Variational Bandwidth Auto-encoder f
Persine is an automated tool to study and reverse-engineer algorithmic recommendation systems.
Persine, the Persona Engine Persine is an automated tool to study and reverse-engineer algorithmic recommendation systems. It has a simple interface a
The official implementation of "DGCN: Diversified Recommendation with Graph Convolutional Networks" (WWW '21)
DGCN This is the official implementation of our WWW'21 paper: Yu Zheng, Chen Gao, Liang Chen, Depeng Jin, Yong Li, DGCN: Diversified Recommendation wi
A library of metrics for evaluating recommender systems
recmetrics A python library of evalulation metrics and diagnostic tools for recommender systems. **This library is activly maintained. My goal is to c
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
Introduction This is the repository of our accepted CIKM 2021 paper "Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Trans
Code for MB-GMN, SIGIR 2021
MB-GMN Code for MB-GMN, SIGIR 2021 For Beibei data, run python .\labcode.py For Tmall data, run python .\labcode.py --data tmall --rank 2 For IJCAI
Code for KHGT model, AAAI2021
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
Movie Recommender System
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
Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021
Detecting Beneficial Feature Interactions for Recommender Systems (L0-SIGN) This is our implementation for the paper: Su, Y., Zhang, R., Erfani, S., &
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation This repository contains the source code of the SIGIR 2019 paper "Reinforcement