RsparseFast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.
Stars: ✭ 145 (+245.24%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system, factorization-machines
DaisyrecA developing recommender system in pytorch. Algorithm: KNN, LFM, SLIM, NeuMF, FM, DeepFM, VAE and so on, which aims to fair comparison for recommender system benchmarks
Stars: ✭ 280 (+566.67%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system, factorization-machines
ImplicitFast Python Collaborative Filtering for Implicit Feedback Datasets
Stars: ✭ 2,569 (+6016.67%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system
Fastfm fastFM: A Library for Factorization Machines
Stars: ✭ 908 (+2061.9%)
Mutual labels: matrix-factorization, recommender-system, factorization-machines
Rectorchrectorch is a pytorch-based framework for state-of-the-art top-N recommendation
Stars: ✭ 121 (+188.1%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system
Flurs🌊 FluRS: A Python library for streaming recommendation algorithms
Stars: ✭ 97 (+130.95%)
Mutual labels: matrix-factorization, recommender-system, factorization-machines
PolaraRecommender system and evaluation framework for top-n recommendations tasks that respects polarity of feedbacks. Fast, flexible and easy to use. Written in python, boosted by scientific python stack.
Stars: ✭ 205 (+388.1%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system
recommender system with Pythonrecommender system tutorial with Python
Stars: ✭ 106 (+152.38%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system
CornacA Comparative Framework for Multimodal Recommender Systems
Stars: ✭ 308 (+633.33%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system
Recsys2019 deeplearning evaluationThis is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
Stars: ✭ 780 (+1757.14%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system
Awesome-Machine-Learning-Papers📖Notes and remarks on Machine Learning related papers
Stars: ✭ 35 (-16.67%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system
ElliotComprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Stars: ✭ 49 (+16.67%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system
RecoderLarge scale training of factorization models for Collaborative Filtering with PyTorch
Stars: ✭ 46 (+9.52%)
Mutual labels: collaborative-filtering, matrix-factorization, recommender-system
RankfmFactorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data
Stars: ✭ 71 (+69.05%)
Mutual labels: collaborative-filtering, recommender-system, factorization-machines
RecotourA tour through recommendation algorithms in python [IN PROGRESS]
Stars: ✭ 140 (+233.33%)
Mutual labels: collaborative-filtering, matrix-factorization
CollaborativememorynetworkCollaborative Memory Network for Recommendation Systems, SIGIR 2018
Stars: ✭ 170 (+304.76%)
Mutual labels: collaborative-filtering, recommender-system
Movielens RecommenderA pure Python implement of Collaborative Filtering based on MovieLens' dataset.
Stars: ✭ 131 (+211.9%)
Mutual labels: collaborative-filtering, recommender-system
SLRCWWW'2019: Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems
Stars: ✭ 32 (-23.81%)
Mutual labels: collaborative-filtering, recommender-system
Recommender SystemA developing recommender system in tensorflow2. Algorithm: UserCF, ItemCF, LFM, SLIM, GMF, MLP, NeuMF, FM, DeepFM, MKR, RippleNet, KGCN and so on.
Stars: ✭ 227 (+440.48%)
Mutual labels: collaborative-filtering, recommender-system
matrix-completionLightweight Python library for in-memory matrix completion.
Stars: ✭ 94 (+123.81%)
Mutual labels: collaborative-filtering, matrix-factorization