RecLearn (Recommender Learning) which summarizes the contents of the master branch in Recommender System with TF2.0 is a recommended learning framework based on Python and TensorFlow2.x for students and beginners. Of course, if you are more comfortable with the master branch, you can clone the entire package, run some algorithms in example, and also update and modify the content of model and layer. The implemented recommendation algorithms are classified according to two application stages in the industry:
- matching recommendation stage (Top-k Recmmendation)
- ranking recommendeation stage (CTR predict model)
RecLearn is on PyPI, so you can use pip to install it.
pip install reclearn
dependent environment：
- python3.7+
- Tensorflow2.7+（It is very important)
- sklearn
Clone Reclearn to local:
git clone -b reclearn git@github.com:ZiyaoGeng/Recommender-System-with-TF2.0.gitIn example, we have given a demo of each of the recommended models.
Firstly，building dataset.
Then, constructing model.
Finally, Compile, Fit and Predict
| Model | ml-1m | Beauty | STEAM | ||||||
|---|---|---|---|---|---|---|---|---|---|
| HR@10 | MRR@10 | NDCG@10 | HR@10 | MRR@10 | NDCG@10 | HR@10 | MRR@10 | NDCG@10 | |
| BPR | 0.5768 | 0.2392 | 0.3016 | 0.3708 | 0.2108 | 0.2485 | 0.7728 | 0.4220 | 0.5054 |
| NCF | 0.5711 | 0.2112 | 0.2950 | 0.5448 | 0.2831 | 0.3451 | 0.7768 | 0.4273 | 0.5103 |
| SASRec | 0.8103 | 0.4812 | 0.5605 | 0.5230 | 0.2781 | 0.3355 | 0.8606 | 0.5669 | 0.6374 |
| Model | 500w(Criteo) | Criteo | ||
|---|---|---|---|---|
| Log Loss | AUC | Log Loss | AUC | |
| FM | 0.4765 | 0.7783 | 0.4762 | 0.7875 |
| FFM | - | - | - | - |
| WDL | 0.4684 | 0.7822 | 0.4692 | 0.7930 |
| Deep Crossing | 0.4670 | 0.7826 | 0.4693 | 0.7935 |
| PNN | - | 0.7847 | - | - |
| DCN | - | 0.7823 | 0.4691 | 0.7929 |
| NFM | 0.4773 | 0.7762 | 0.4723 | 0.7889 |
| AFM | 0.4819 | 0.7808 | 0.4692 | 0.7871 |
| DeepFM | - | 0.7828 | 0.4650 | 0.8007 |
| xDeepFM | 0.4690 | 0.7839 | 0.4696 | 0.7919 |
| Paper|Model | Published | Author |
|---|---|---|
| BPR: Bayesian Personalized Ranking from Implicit Feedback|MF-BPR | UAI, 2009 | Steffen Rendle |
| Neural network-based Collaborative Filtering|NCF | WWW, 2017 | Xiangnan He |
| Self-Attentive Sequential Recommendation｜SASRec | ICDM, 2018 | UCSD |
| Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding｜Caser | WSDM, 2018 | Jiaxi Tang |
| Next Item Recommendation with Self-Attentive Metric Learning|AttRec | AAAAI, 2019 | Shuai Zhang |
| Paper｜Model | Published | Author |
|---|---|---|
| Factorization Machines|FM | ICDM, 2010 | Steffen Rendle |
| Field-aware Factorization Machines for CTR Prediction｜FFM | RecSys, 2016 | Criteo Research |
| Wide & Deep Learning for Recommender Systems｜WDL | DLRS, 2016 | Google Inc. |
| Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features|Deep Crossing | KDD, 2016 | Microsoft Research |
| Product-based Neural Networks for User Response Prediction|PNN | ICDM, 2016 | Shanghai Jiao Tong University |
| Deep & Cross Network for Ad Click Predictions｜DCN | ADKDD, 2017 | Stanford University｜Google Inc. |
| Neural Factorization Machines for Sparse Predictive Analytics|NFM | SIGIR, 2017 | Xiangnan He |
| Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks|AFM | IJCAI, 2017 | Zhejiang University|National University of Singapore |
| DeepFM: A Factorization-Machine based Neural Network for CTR Prediction|DeepFM | IJCAI, 2017 | Harbin Institute of Technology|Noah’s Ark Research Lab, Huawei |
| xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems|xDeepFM | KDD, 2018 | University of Science and Technology of China |
| Deep Interest Network for Click-Through Rate Prediction|DIN | KDD, 2018 | Alibaba Group |
- If you have any suggestions or questions about the project, you can leave a comment on
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