Scikit-Recommender is an open source library for researchers of recommender systems.
- 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
You have three ways to use Scikit-Recommender:
- Install from PyPI
- Install from Source
- Run without Installation
Binary installers are available at the Python package index and you can install the package from pip.
pip install scikit-recommender
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
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
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.
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 |