Implements of Awesome RecSystem Models with PyTorch/TF2.0

1. Requirements

TensorFlow2.0, PyTorch1.2+, Python3.6, NumPy, sk-learn, Pandas

2. DataSets

Criteo
- This dataset Contains about 45 million records. There are 13 features taking integer values (mostly count features) and 26 categorical features.
- The columns are tab separated with the following sechema: <int feat 1>...<int feat 13><cate feat 1>...
- The dataset is available at http://labs.criteo.com/2014/02/download-kaggle-display-advertising-challenge-dataset/
- Put the downloaded 'train.txt' file in 'data/Criteo/' folder.
Movielens100K
- MovieLens 100K movie ratings. Stable benchmark dataset for Recommendation System.
- This dataset contain 100,000 ratings from 1000 users on 1700 movies.
- The details can be found at https://grouplens.org/datasets/movielens/100k/
- This dataset have been downloaded and is available at 'data/Movielens100K'

3. Implemented Models:

FM: Factorization Machine
- The paper is available at: https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
- Tested dataset: Criteo
- Split the data set by 9:1 for train and test.
- How To Run: Run data/forOtherModels/dataPreprocess_PyTorch.py to pre-process the data, then run Model/FM_PyTorch.py. Or run data/forOtherModels/dataPreprocess_TensorFlow.py to pre-process the data, and run Model/FM_TensorFlow.py
- the result is AUC: 0.7805(PyTorch), 0.7791(TensorFlow)
- For Multi-Classification implements:
  - Tested dataset: Movielens100K
  - Run FM_Multi_PyTorch.py
FFM: Field-aware Factorization Machine
- The paper is available at: https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf
- Tested dataset: Movielens100K
- Support Multi-Classification
DeepFM: Factorization-Machine based Neural Network
- The paper is available at: https://www.ijcai.org/proceedings/2017/0239.pdf
- Tested dataset: Criteo
- Split the data set by 9:1 for train and test.
- How To Run: Run data/forOtherModels/dataPreprocess_PyTorch.py to pre-process the data, then run Model/DeepFM_PyTorch.py. Or run data/forOtherModels/dataPreprocess_TensorFlow.py to pre-process the data, and run Model/DeepFM_TensorFlow.py
- PyTorch: After 3 Epochs, AUC: 0.795(The paper result is 0.801)
- TensorFlow: After 3 Epochs, AUC: 0.8014, LogLoss: 0.4516
DCN: Deep&Cross Network
- The paper is available at: https://arxiv.org/pdf/1708.05123.pdf
- Tested dataset: Criteo
- Run data/forDCN/DCN_dataPreprocess_PyTorch.py to pre-process the data. According to the paper, the data set is split by 9:0.5:0.5 for train, test and valid
- Split the data set by 9:0.5:0.5 for train, test and valid
- Run Model/DeepCrossNetwork_PyTorch.py, and the results are as follows:
  
  Epochs AUC LogLoss
  
  1st 0.80157 0.45192
  
  2nd 0.80430 0.44922
  
  3rd 0.80546 0.44817
  
  4th 0.80639 0.44729
  
  5th 0.80696 0.44678
xDeepFM: eXtreme Deep Factorization Machine
- The Paper is available at: https://arxiv.org/pdf/1803.05170.pdf
- Tested dataset: Criteo
- Split the data set by 9:1 for train and test.
- Run data/forXDeepFM/xDeepFM_dataPreprocess_PyTorch.py to pre-process the data. The pre-process of xDeepFM is identical with that of DeepFM.
- Run Model/xDeepFM_PyTorch.py.
- After 5 epochs, the result is AUC 0.80148, LogLoss 0.45104 (The paper result is AUC 0.8052).
PNN: Product-based Neural Network
- Paper: https://arxiv.org/pdf/1611.00144.pdf
- Tested dataset: Criteo
- Split the data set by 9:1 for train and test.
- How To Run: Run data/forOtherModels/dataPreprocess_PyTorch.py to pre-process the data, then run Model/ProductNeuralNetwork_PyTorch.py
- After 5 epochs, the results are AUC:
  
  Framework(Algorithm) AUC LogLoss
  
  PyTorch(IPNN) 0.76585 0.47766
  
  PyTorch(OPNN) 0.77656 0.46988
  
  TensorFlow(IPNN) 0.77996 0.46827
  
  TensorFlow(OPNN) 0.78098 0.46718

Epochs	AUC	LogLoss
1st	0.80157	0.45192
2nd	0.80430	0.44922
3rd	0.80546	0.44817
4th	0.80639	0.44729
5th	0.80696	0.44678

Framework(Algorithm)	AUC	LogLoss
PyTorch(IPNN)	0.76585	0.47766
PyTorch(OPNN)	0.77656	0.46988
TensorFlow(IPNN)	0.77996	0.46827
TensorFlow(OPNN)	0.78098	0.46718

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awesome-recsystem-models's Introduction

Implements of Awesome RecSystem Models with PyTorch/TF2.0

1. Requirements

2. DataSets

Criteo

Movielens100K

3. Implemented Models:

FM: Factorization Machine

FFM: Field-aware Factorization Machine

DeepFM: Factorization-Machine based Neural Network

DCN: Deep&Cross Network

xDeepFM: eXtreme Deep Factorization Machine

PNN: Product-based Neural Network

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