├── .gitignore ├── .idea ├── Recommender System.iml ├── misc.xml ├── modules.xml ├── vcs.xml └── workspace.xml ├── Collaborative Filtering ├── Cambridge.py ├── FILTERINGDATA.py ├── ItemBasedCF (2).py ├── ItemBasedCF.py ├── RMSE.py ├── Recommender │ └── Recommender.py ├── Test.py ├── UBCFRMSE.py ├── UserBasedCF.py ├── kNNIBCF.py └── kNNUBCF.py ├── ConvMF ├── README.md ├── __init__.py ├── data_manager.py ├── models.py ├── run.py ├── run_test_ConvMF.sh ├── run_test_preprocess.sh ├── text_analysis │ ├── __init__.py │ └── models.py └── util.py ├── Data ├── ml-100k │ └── Note.txt └── ml-1m │ └── Note.txt ├── Deep-Cross-Net ├── data │ ├── test.csv │ └── train.csv └── src │ ├── DCN.py │ ├── DataLoader.py │ ├── config.py │ └── main.py ├── Demo.py ├── Docs ├── Collaborative Filtering with User Ratings and Tags.pdf ├── Leveraging Tagging for Neighborhood-aware Probabilistic Matrix Factorization.pdf ├── Probabilistic Matrix Factorization.pptx ├── Probabilistic-matrix-factorization.pdf ├── Tag Informed Collaborative Filtering.pdf ├── UBCF.png └── 基于时序行为的协同过滤推荐算法.pdf ├── Factorization-Machine └── FM_model.py ├── Field-aware-Factorization-Machine └── FFM_model.py ├── ItemBasedCF.py ├── MovieLens Test.ipynb ├── Probabilistic Matrix Factorization ├── Probabilistic-Matrix-Factorization.py └── README.md ├── README.md ├── UserBasedCF.py └── loadMovieLens.py /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fuhailin/Recommender-System/HEAD/.gitignore -------------------------------------------------------------------------------- /.idea/Recommender System.iml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fuhailin/Recommender-System/HEAD/.idea/Recommender System.iml -------------------------------------------------------------------------------- /.idea/misc.xml: 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-------------------------------------------------------------------------------- 1 | # Recommender-System 2 | 利用MovieLens数据，Pearson相似度，分别基于User和Item构建一个简单的kNN推荐系统，并给出RMSE评测 3 | -------------------------------------------------------------------------------- /UserBasedCF.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fuhailin/Recommender-System/HEAD/UserBasedCF.py -------------------------------------------------------------------------------- /loadMovieLens.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fuhailin/Recommender-System/HEAD/loadMovieLens.py --------------------------------------------------------------------------------