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rec_now

recommend now (rec_now) 是一个推荐算法的基础库，目的在于简化推荐模型的开发。 本项目基于tensorflow2和python3实现，兼容使用tensorflow1.x API的[无量训练框架](https://git.woa.com/deep_learning_framework/NumerousTensorFlow)，并在生产环境中得到了充分验证。 其中，基于in-batch方式计算pairwise的方式，已经在QQ浏览器信息流的精排、粗排、召回排序等环节得到了广泛应用，并使得在线GAUC指标提升1%以上。

recsyspapers

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

reginx

Build a recommendation system from scratch

rul_pm

Remaining useful life estimation utilities

rust-bert

Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)

satrans

The source code for our paper "Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction" (accepted by KDD2023 Applied Science Track), which proposes a model for Multi-Scenario/Multi-Domain Recommendation.

scikit-uplift

:exclamation: uplift modeling in scikit-learn style in python :snake:

site

sparkcore

Spark源码分析,主要包含SparkContext源码、Executor进程启动、Stage划分、Task执行和Spark2.0的新特性

stable-diffusion-webui

Stable Diffusion web UI

stellargraph

StellarGraph - Machine Learning on Graphs

tensorflow-serving-tutorial

A tutorial of building tensorflow serving service from scratch

tf_repos

TensorFlow Script

the-algorithm

Source code for Twitter's Recommendation Algorithm

torch-rechub

A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.

tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

uplift-modeling

Final project for ML 2021 course in Skoltech

www-22-dihn

[WWW'22] Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation

xgboost-serving

A flexible, high-performance serving system for machine learning models