A simple package about learning recommendation
Project description
RecLearn
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. 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)
Installation
RecLearn is on PyPI, so you can use pip
to install it.
pip install reclearn
dependent environment:
- python3.7+
- Tensorflow2.6+
- sklearn
Quick Start
In example, we have given a demo of each of the recommended models.
Firstly,building dataset.
Then, constructing model.
Finally, Compile, Fit and Predict
Model List
1. Matching Stage
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 |
2. Ranking Stage
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 |
Discussion
- If you have any suggestions or questions about the project, you can leave a comment on
Issue
or emailzggzy1996@163.com
. - wechat:
Project details
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