fastgreen

Recommender_System. 推荐系统入门指南，全面介绍了工业级推荐系统的理论知识（王树森推荐系统公开课-基于小红书的场景讲解工业界真实的推荐系统），如何基于TensorFlow2训练模型，如何实现高性能、高并发、高可用的Golang推理微服务。Comprehensively introduced the theory of industrial recommender system, how to trainning models based on TensorFlow2, how to implement the high-performance、high-concurrency and high-available inference services base on Golang.

DNN_for_YouTube_Recommendations. YouTube推荐系统深度学习召回排序算法, Deep Neural Networks for YouTube Recommendations. YouTubeDNN.

Machine_Learning_Sklearn_Examples. 机器学习Sklearn入门指南。Machine Learning Sklearn API and Examples with Python3 and Jupyter Notebook.

Recommender_System_Inference_Services. Large scale recommender system inference Microservices and APIs （Dubbo 、gRPC and REST ） with Golang.

Deep_Learning_TensorFlow2_Examples. 深度学习TensorFlow2入门指南。Deep Learning TensorFlow2 API and Examples with Python3 and Jupyter Notebook.

Classic_Papers_On_Recommender_System. 这里介绍了一些推荐系统经典论文，适合初学者学习使用。Here is my Google Scholar profile, where you can find some papers related to recommendation systems. If they are helpful to you, feel free to cite them.