https://github.com/JianzhouZhan/Awesome-RecSystem-Models
Implements of Awesome RecSystem Models with PyTorch/TF2.0
https://github.com/JianzhouZhan/Awesome-RecSystem-Models
List: Awesome-RecSystem-Models
ctr dcn deepfm ffm fm pnn pytorch tensorflow2 xdeepfm
Last synced: about 2 months ago
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Implements of Awesome RecSystem Models with PyTorch/TF2.0
- Host: GitHub
- URL: https://github.com/JianzhouZhan/Awesome-RecSystem-Models
- Owner: JianzhouZhan
- License: mit
- Created: 2019-09-05T10:47:59.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-04-04T05:44:50.000Z (about 5 years ago)
- Last Synced: 2025-04-30T12:20:19.124Z (about 2 months ago)
- Topics: ctr, dcn, deepfm, ffm, fm, pnn, pytorch, tensorflow2, xdeepfm
- Language: Python
- Homepage:
- Size: 916 KB
- Stars: 257
- Watchers: 7
- Forks: 68
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Tensorflow2 - JianzhouZhan/Awesome-RecSystem-Models
- awesome-list - Awesome-RecSystem-Models - Implements of Awesome RecSystem Models with PyTorch/TF2.0. (Recommendation, Advertisement & Ranking / Others)
README
# 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: ......
- 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|