Recommender System WITH PyTorch 🟢🟠🔴
PyTorch Implementation of classic Recommender System Models mainly used for self-learing&communication.
checkout for tensorflow branch
corresponding papers 🚀 RS_Papers 📖
Matching
Matrix Factorization
| Model |
dataset |
loss_func |
metrics |
state |
| LFM |
ml-100k |
MSELoss |
MSE: 0.9031 |
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| BiasSVD |
ml-100k |
MSELoss |
MSE: 0.8605 |
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| SVD++ |
ml-100k |
MSELoss |
MSE: 0.8493 |
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Factorization Machine
| Model |
dataset |
loss_func |
metrics |
state |
| FM |
criteo |
BCELoss |
AUC: 0.6934 |
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| FFM |
criteo |
BCELoss |
AUC: 0.6729 |
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Sequential based
| Model |
dataset |
loss_func |
metrics |
state |
| FPMC |
ml-100k |
sBPRLoss |
Recall@10: 0.0622 |
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| SASRec |
ml-100k |
BCEWithLogitsLoss |
NDCG@10: 0.1801 HR@10: 0.3595 |
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Knowledge aware
| Model |
dataset |
loss_func |
metrics |
state |
| RippleNet |
ml-1m |
BCELoss |
AUC: 0.8838 |
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Graph embedding
Point of Interests
CF
| Model |
dataset |
loss_func |
metrics |
state |
| NeuralCF |
ml-100k |
MSELoss |
MSE: 0.3322 |
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Ranking
FM
| Model |
dataset |
loss_func |
metrics |
state |
| FNN |
criteo |
BCELoss |
AUC: 0.6787 |
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| DeepFM |
criteo |
BCELoss |
AUC: 0.6854 |
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| NFM |
criteo |
BCELoss |
AUC: 0.6705 |
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| AFM |
criteo |
BCELoss |
AUC: 0.6572 |
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LR
DNN
| Model |
dataset |
loss_func |
metrics |
state |
| Deep Crossing |
criteo |
BCELoss |
AUC: 0.7210 |
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| PNN |
criteo |
BCELoss |
AUC: 0.6360 |
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| Wide&Deep |
criteo |
BCELoss |
AUC: 0.7074 |
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| DCN |
criteo |
BCELoss |
AUC: 0.7335 |
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| DIN |
amazon book |
BCELoss |
AUC: 0.5988 |
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DIN: It seems that the feature engineering(negative sampling) of paper used for amazon book seems bad. I try hard but the auc of test cannot reach the 0.811 on amazon book.
Multi tasks
| Model |
dataset |
loss_func |
metrics |
state |
| MMOE |
census-income |
BCEWithLogitsLoss |
income-AUC: 0.9061 marry-AUC: 0.9637 |
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| ESMM |
census-income |
BCEWithLogitsLoss |
income-ctr-AUC: 0.9242 ctcvr-AUC: 0.9122 |
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RS-Models copied to clipboard
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