Description:

scikitrecommender 0.1.1

Scikit-Recommender

Scikit-Recommender is an open source library for researchers of recommender systems.
Highlighted Features

Various recommendation models
Parse arguments from command line and ini-style files
Diverse data preprocessing
Fast negative sampling
Fast model evaluation
Convenient record logging
Flexible batch data iterator

Installation

You have three ways to use Scikit-Recommender:

Install from PyPI
Install from Source
Run without Installation

Install from PyPI
Binary installers are available at the Python package index and you can install the package from pip.
pip install scikit-recommender

Install from Source
Installing from source requires Cython and the current code works well with the version 0.29.20.
To build scikit-recommender from source you need Cython:
pip install cython==0.29.20

Then, the scikit-recommender can be installed by executing:
git clone https://github.com/ZhongchuanSun/scikit-recommender.git
cd scikit-recommender
python setup.py install

Run without Installation
Alternatively, You can also run the sources without installation.
Please compile the cython codes before running:
git clone https://github.com/ZhongchuanSun/scikit-recommender.git
cd scikit-recommender
python setup.py build_ext --inplace

Usage
After installing or compiling this package, now you can run the run_skrec.py:
python run_skrec.py

You can also find examples in tutorial.ipynb.
Models



MMRec
Implementation
Paper
  Publication  




MGCN
PyTorch
Penghang Yu, et al., Multi-View Graph Convolutional Network for Multimedia Recommendation
ACM MM 2023


BM3
PyTorch
Xin Zhou, et al., Bootstrap Latent Representations for Multi-modal Recommendation
WWW 2023


FREEDOM
PyTorch
Xin Zhou, et al., A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation
ACM MM 2023


SLMRec
PyTorch
Zhulin Tao, et al., Self-supervised Learning for Multimedia Recommendation
TMM 2022


LATTICE
PyTorch
Jinghao Zhang, et al., Mining Latent Structures for Multimedia Recommendation
ACM MM 2021






Recommender
Implementation
Paper
  Publication  




SelfCF
PyTorch
Xin Zhou, et al., SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
TORS 2023


LayerGCN
PyTorch
Xin Zhou, et al., Layer-refined Graph Convolutional Networks for Recommendation
ICDE 2023


DENS
PyTorch
Riwei Lai, et al., Disentangled Negative Sampling for Collaborative Filtering
WSDM 2023


LightGCL
PyTorch
Xuheng Cai, et al., LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
ICLR 2023


SGAT
TensorFlow (1.14)
Zhongchuan Sun, et al., Sequential Graph Collaborative Filtering
Information Sciences 2022


LightGCN
PyTorch
Xiangnan He et al., LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation.
SIGIR 2020


SRGNN
TensorFlow (1.14)
Shu Wu et al., Session-Based Recommendation with Graph Neural Networks.
AAAI 2019


HGN
PyTorch
Chen Ma et al., Hierarchical Gating Networks for Sequential Recommendation.
KDD 2019


BERT4Rec
TensorFlow (1.14)
Fei Sun et al., BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer.
CIKM 2019


SASRec
TensorFlow (1.14)
Wangcheng Kang et al., Self-Attentive Sequential Recommendation.
ICDM 2018


GRU4RecPlus
TensorFlow (1.14)
Balázs Hidasi et al., Recurrent Neural Networks with Top-k Gains for Session-based Recommendations.
CIKM 2018


Caser
PyTorch
Jiaxi Tang et al., Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding.
WSDM 2018


MultiVAE
PyTorch
Dawen Liang, et al., Variational Autoencoders for Collaborative Filtering.
WWW 2018


TransRec
PyTorch
Ruining He et al., Translation-based Recommendation.
RecSys 2017


CML
TensorFlow (1.14)
Cheng-Kang Hsieh et al., Collaborative Metric Learning.
WWW 2017


CDAE
PyTorch
Yao Wu et al., Collaborative Denoising Auto-Encoders for Top-n Recommender Systems.
WSDM 2016


GRU4Rec
TensorFlow (1.14)
Balázs Hidasi et al., Session-based Recommendations with Recurrent Neural Networks.
ICLR 2016


AOBPR
C/Cython
Steffen Rendle et al., Improving Pairwise Learning for Item Recommendation from Implicit Feedback.
WSDM 2014


FPMC
PyTorch
Steffen Rendle et al., Factorizing Personalized Markov Chains for Next-Basket Recommendation.
WWW 2010


BPRMF
PyTorch
Steffen Rendle et al., BPR: Bayesian Personalized Ranking from Implicit Feedback.
UAI 2009


Pop
Python
Make recommendations based on item popularity.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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