Rank

Name: tangxyw/RecAlgorithm
Brand: tangxyw/RecAlgorithm
SKU: project/tangxyw/RecAlgorithm
Rating: 4.44 (48 reviews)

Model	Paper	*Best_read_comment_Auc
FFM	[2016] Field-aware Factorization Machines for CTR Prediction	0.8911285
DeepCrossing	[2016] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features	0.9185908
PNN	[2016] Product-based neural networks for user response prediction	0.9065931
Wide & Deep	[2016] Wide & Deep Learning for Recommender Systems	0.9133482
DeepFM	[2017] DeepFM: A Factorization-Machine based Neural Network for CTR Prediction	0.8529998
DCN	[2017] Deep & Cross Network for Ad Click Predictions	0.9183242
AFM	[2017] Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks	0.9117872
xDeepFM	[2018] xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems	0.9152467
FwFM	[2018] Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising	0.9118794
DIN	[2018] Deep Interest Network for Click-Through Rate Prediction	0.9116896
DIEN	[2018] Deep Interest Evolution Network for Click-Through Rate Prediction	-
FiBiNet	[2019] FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction	0.9149044
BST	[2019] Behavior sequence transformer for e-commerce recommendation in Alibaba	0.9165866

*Best_read_comment_AucmodelAucmodelmodelresult.md *DIEN

Model	Paper	*Best_read_commet_AUC	*Best_like_AUC	*Best_click_avatar_AUC
ESMM	[2018] Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate	-	-	-
MMOE	[2018] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts	0.91860557	0.8126400	0.8139362
PLE	[2020] Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations	0.91965175	0.8136461	0.8154559

*Best_xx_AUCAUC *ESMM

# tfrecord
# cd ./dataset/wechat_algo_data1
# python DataGenerator.py && cd ..
cd ./DIN
# 
python din.py --use_softmax=True

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