reclearn 1.0.6

A simple package about learning recommendation

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+
• Tensorflow2.6+
• 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

1. If you have any suggestions or questions about the project, you can leave a comment on Issue or email [e-mail].
2. wechat：