Package: scikit-recommender
Scikit-RecommenderScikit-Recommender is an open source library for researchers of recommender systems.Highlighted FeaturesVarious recommendation modelsParse arguments from command line and ini-style filesDiverse data preprocessingFast negative samplingFast model evaluationConvenient record loggingFlexible batch data iteratorInstallationYou have three ways to use Scikit-Recommender:Install from PyPIInstall from SourceRun without InstallationInstall from PyPIBinary installers are available at thePython package indexand you can install the package from pip.pipinstallscikit-recommenderInstall from SourceInstalling 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:pipinstallcython==0.29.20Then, the scikit-recommender can be installed by executing:gitclonehttps://github.com/ZhongchuanSun/scikit-recommender.gitcdscikit-recommender
pythonsetup.pyinstallRun without InstallationAlternatively, You can also run the sources without installation.
Please compile the cython codes before running:gitclonehttps://github.com/ZhongchuanSun/scikit-recommender.gitcdscikit-recommender
pythonsetup.pybuild_ext--inplaceUsageAfter installing or compiling this package, now you can run therun_skrec.py:pythonrun_skrec.pyYou can also find examples intutorial.ipynb.ModelsMMRecImplementationPaperPublicationMGCNPyTorchPenghang Yu, et al., Multi-View Graph Convolutional Network for Multimedia RecommendationACM MM 2023BM3PyTorchXin Zhou, et al., Bootstrap Latent Representations for Multi-modal RecommendationWWW 2023FREEDOMPyTorchXin Zhou, et al., A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal RecommendationACM MM 2023SLMRecPyTorchZhulin Tao, et al., Self-supervised Learning for Multimedia RecommendationTMM 2022LATTICEPyTorchJinghao Zhang, et al., Mining Latent Structures for Multimedia RecommendationACM MM 2021RecommenderImplementationPaperPublicationSelfCFPyTorchXin Zhou, et al., SelfCF: A Simple Framework for Self-supervised Collaborative FilteringTORS 2023LayerGCNPyTorchXin Zhou, et al., Layer-refined Graph Convolutional Networks for RecommendationICDE 2023DENSPyTorchRiwei Lai, et al., Disentangled Negative Sampling for Collaborative FilteringWSDM 2023LightGCLPyTorchXuheng Cai, et al., LightGCL: Simple Yet Effective Graph Contrastive Learning for RecommendationICLR 2023SGATTensorFlow (1.14)Zhongchuan Sun, et al., Sequential Graph Collaborative FilteringInformation Sciences 2022LightGCNPyTorchXiangnan He et al., LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation.SIGIR 2020SRGNNTensorFlow (1.14)Shu Wu et al., Session-Based Recommendation with Graph Neural Networks.AAAI 2019HGNPyTorchChen Ma et al., Hierarchical Gating Networks for Sequential Recommendation.KDD 2019BERT4RecTensorFlow (1.14)Fei Sun et al., BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer.CIKM 2019SASRecTensorFlow (1.14)Wangcheng Kang et al., Self-Attentive Sequential Recommendation.ICDM 2018GRU4RecPlusTensorFlow (1.14)Balázs Hidasi et al., Recurrent Neural Networks with Top-k Gains for Session-based Recommendations.CIKM 2018CaserPyTorchJiaxi Tang et al., Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding.WSDM 2018MultiVAEPyTorchDawen Liang, et al., Variational Autoencoders for Collaborative Filtering.WWW 2018TransRecPyTorchRuining He et al., Translation-based Recommendation.RecSys 2017CMLTensorFlow (1.14)Cheng-Kang Hsieh et al., Collaborative Metric Learning.WWW 2017CDAEPyTorchYao Wu et al., Collaborative Denoising Auto-Encoders for Top-n Recommender Systems.WSDM 2016GRU4RecTensorFlow (1.14)Balázs Hidasi et al., Session-based Recommendations with Recurrent Neural Networks.ICLR 2016AOBPRC/CythonSteffen Rendle et al., Improving Pairwise Learning for Item Recommendation from Implicit Feedback.WSDM 2014FPMCPyTorchSteffen Rendle et al., Factorizing Personalized Markov Chains for Next-Basket Recommendation.WWW 2010BPRMFPyTorchSteffen Rendle et al., BPR: Bayesian Personalized Ranking from Implicit Feedback.UAI 2009PopPythonMake recommendations based on item popularity.