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A simple package about learning recommendation

Project description

RecLearn

简体中文English

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+
  • Tensorflow2.7+(It is very important)
  • 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

  1. If you have any suggestions or questions about the project, you can leave a comment on Issue or email zggzy1996@163.com.
  2. wechat:

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