Best Practices on Recommendation Systems
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Updated
Jun 29, 2026 - Python
Best Practices on Recommendation Systems
OpenVINO™ is an open source toolkit for optimizing and deploying AI inference
深度学习面试宝典(含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向)
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
A unified, comprehensive and efficient recommendation library
Fast Python Collaborative Filtering for Implicit Feedback Datasets
Pytorch domain library for recommendation systems
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.
计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估
Minimal reproduction of OneRec
A TensorFlow recommendation algorithm and framework in Python.
A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.
An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
本地私有的跨平台 AI 内容发现 Agent——先深度理解你,再主动去 B站/小红书/抖音/YouTube/X/知乎/Reddit 找你会喜欢的内容。Local-first AI agent that learns who you are, then hunts content you'll love across platforms.
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training
A Comparative Framework for Multimodal Recommender Systems
AI-related tutorials. Access any of them for free → https://towardsai.net/editorial
推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction
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