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
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.
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.
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
Open CLI for integrating AI search, recommendation, and conversational retrieval into agent systems and business systems
AI-related tutorials. Access any of them for free → https://towardsai.net/editorial
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