CTR-master.zip （92个子文件） 
CTR-master 
.gitignore 183B README.md 3KB config.py 2KB const frappe_const.py 93B amazon_const.py 731B census_const.py 2KB __init__.py 731B model FNN __init__.py 0B preprocess.py 580B FNN.py 2KB PNN PNN.py 3KB __init__.py 0B preprocess.py 660B DCN DCN.py 5KB __init__.py 0B preprocess.py 659B AFM AFM.py 5KB __init__.py 0B preprocess.py 786B xDeepFM xDeepFM.py 5KB __init__.py 0B preprocess.py 786B DeepCrossing DeepCrossing.py 2KB __init__.py 0B preprocess.py 659B FFM __init__.py 0B preprocess.py 638B FFM.py 2KB EMMLP __init__.py 0B preprocess.py 959B EMMLP.py 3KB FM FM_keras.py 4KB __init__.py 0B FM.py 2KB preprocess.py 580B FiBiNET __init__.py 0B preprocess.py 786B FiBiNET.py 8KB DIEN preprocess.py 0B DeepFM DeepFM.py 6KB __init__.py 0B preprocess.py 786B NFM __init__.py 0B preprocess.py 785B NFM.py 4KB wide_and_deep __init__.py 0B preprocess.py 2KB wide_and_deep.py 1KB DIN DIN.py 5KB __init__.py 0B preprocess.py 498B utils.py 6KB main.py 3KB __init__.py 0B paper 
[xDeepFM]xDeepFM- Combining Explicit and Implicit Feature Interactions for Recommender Systems.pdf 1.44MB [AutoInt]- Automatic Feature Interaction Learning via Self-Attentive Neural Networks.pdf 1.52MB [PNN] Product-based Neural Networks for User Response Prediction (SJTU 2016).pdf 470KB [FFM]Field-aware Factorization Machines for CTR Prediction.pdf 361KB [DIN]Deep Interest Network for Click-Through Rate Prediction.pdf 8.13MB [AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks (ZJU 2017).pdf 3.39MB [FM Method]Factorization Machines.pdf 187KB [AFM]Attentional Factorization Machines- Learning the Weight of Feature Interactions via Attention Networks∗.pdf 987KB [NCF] Neural Collaborative Filtering (NUS 2017).pdf 1.42MB [Wide&Deep] Wide & Deep Learning for Recommender Systems (Google 2016).pdf 480KB [Deep&Cross]Deep & Cross Network for Ad Click Predictions.pdf 232KB [Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016).pdf 434KB [NFM]Neural Factorization Machines for Sparse Predictive Analytics.pdf 3.49MB [DeepFM]DeepFM- A Factorization-Machine based Neural Network for CTR Prediction.pdf 1.14MB [FNN] Deep Learning over Multi-field Categorical Data (UCL 2016).pdf 566KB [GBDT+LR]Practical Lessons from Predicting Clicks on Ads at Facebook.pdf 774KB [DIEN]Deep Interest Evolution Network for Click-Through Rate Prediction.pdf 2.07MB [FiBiNET]- Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction.pdf 939KB [FM Model] Fast Context-aware Recommendations with Factorization Machines (UKON 2011).pdf 291KB playground Embedding Embedding.py 3KB feature_columnn feature_column_play.ipynb 15KB layers.py 2KB data amazon 2_remap_id.py 2KB 0_download_raw.sh 310B 1_convert_pd.py 665B readme.md 3KB 3_build_dataset.py 1KB 4_dump_tfrecord.py 2KB movie_len ratings.csv 2.37MB frappe frappe.test.libfm 1.98MB readme.md 2KB frappe.train.libfm 13.89MB frappe.valid.libfm 3.97MB ali_ccp readme.md 407B census train.csv 3.36MB data_loader.py 922B readme.md 2KB valid.csv 1.68MB
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