Easy-to-use implementations of well-known recommender system algorithms based on Python Tensorflow 2.
Project description
## Introduction
easyrec is an open-sourced and easy-to-use recommender system toolbox based on tensorflow 2.
## License
This project is released under the [MIT License](https://github.com/xu-zhiwei/easyrec/blob/main/LICENSE).
## Features | model | source | | —- | —- | | Logisitic Regression (LR) | | | Multi-layer Perceptron (MLP) | | | Deep Structured Semantic Model (DSSM) | Po-Sen Huang et al. Learning Deep Structured Semantic Models for Web Search using Clickthrough Data. CIKM. 2013. | | Factorization Machine (FM) | Steffen Rendle. Factorization Machines. ICDM. 2010. | | Deep Crossing | Ying Shan et al. Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features. KDD. 2016. | | Factorization-machine supported Neural Network (FNN) | Weinan Zhang. Deep Learning over Multi-field Categorical Data – A Case Study on User Response Prediction. ECIR. 2016. | | Product-based Neural Network (PNN) | Yanru Qu et al. Product-based Neural Networks for User Response Prediction. ICDM. 2016. | | Wide & Deep | Heng-Tze Cheng et al. Wide & Deep Learning for Recommender Systems. RecSys. 2016. | | Field-aware Factorization Machine (FFM) | Yuchin Juan et al. Field-aware Factorization Machines for CTR Prediction. RecSys. 2016. | | Attentional Factorization Machine (AFM) | Jun Xiao et al. Attentional Factorization Machines:Learning the Weight of Feature Interactions via Attention Networks. arXiv. 2017. | | Neural Factorization Machine (NFM) | Xiangnan He et al. Neural Factorization Machines for Sparse Predictive Analytics. 2017. SIGIR. | | DeepFM | Huifeng Guo et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. arXiv. 2017. | | Multi-gate Mixture-of-Experts (MMOE) | Jiaqi Ma et al. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts. KDD. 2018. |
## Getting Started Please refer to the [documentation]() for the basic usage of easyrec.
## Note The document is working in progress and will be released very soon! Thank you for your attention.
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