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Matrix-Factorization-for-Recommendation
Matrix-Factorization-for-Recommendation PublicUsing Matrix Factorization/Probabilistic Matrix Factorization to solve Recommendation。矩阵分解进行推荐系统算法。
R 2
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Matrix-Factorization-Implicit-Feedback
Matrix-Factorization-Implicit-Feedback Public使用矩阵分解算法处理隐式反馈数据,并进行Top-N推荐。The matrix factorization algorithm is used to process the implicit feedback data and make top-N recommendation.
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NCF-MF-for-Recommendation
NCF-MF-for-Recommendation Public分别使用传统方法(KNN,SVD,NMF等)和深度方法(NCF)进行推荐系统的评分预测。Traditional methods (KNN, SVD, NMF, etc.) and depth method (NCF) were used to predict rating of the recommendation system.
Jupyter Notebook 6
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P300-BCI-Data-Analysis
P300-BCI-Data-Analysis Public2020年研究生数学建模竞赛C题,全国二等奖,分析脑机接口数据进行分析预测。The data of BCI were analyzed and predicted.
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multi-factor-strategy-joinquant
multi-factor-strategy-joinquant Public在聚宽(joinquant)平台上使用多因子策略进行量化投资模拟。
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CVPR-2020-LEAP
CVPR-2020-LEAP PublicUnofficial implement of LEAP(Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective) for Multi-Label Classification.
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