PyTorch Implementation of classic Recommender System Models mainly used for self-learing&communication.
checkout for tensorflow branch
corresponding papers 🚀 RS_Papers 📖
| 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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| 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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| 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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| Model | dataset | loss_func | metrics | state |
|---|---|---|---|---|
| RippleNet | ml-1m |
BCELoss | AUC: 0.8838 |
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| DeepWalk | Node2vec | EGES |
|---|
| MIND | SDM |
|---|
| Model | dataset | loss_func | metrics | state |
|---|---|---|---|---|
| NeuralCF | ml-100k |
MSELoss | MSE: 0.3322 |
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| 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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| GBDT+LR |
|---|
| 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 bookseems bad. I try hard but the auc of test cannot reach the0.811onamazon book.
| 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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