DeepRec and RecSys

DeepRec
51
Established
RecSys
43
Emerging
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 0/25
Adoption 10/25
Maturity 8/25
Community 25/25
Stars: 1,021
Forks: 219
Downloads:
Commits (30d): 0
Language:
License: MIT
Stars: 2,073
Forks: 440
Downloads:
Commits (30d): 0
Language:
License:
Stale 6m No Package No Dependents
No License Stale 6m No Package No Dependents

About DeepRec

imsheridan/DeepRec

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

This is a curated collection of research papers and industry insights focused on recommendation systems and predicting click-through rates (CTR) in advertising. It provides a structured overview of classic and cutting-edge techniques. E-commerce strategists, advertising specialists, and data scientists looking to enhance their recommendation engines and ad targeting models would find this helpful for staying current with industry best practices.

e-commerce recommendations digital advertising customer engagement ad optimization personalization

About RecSys

mJackie/RecSys

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

This resource curates learning materials for professionals working on recommendation systems and computational advertising. It provides a comprehensive collection of articles, practical tools, code examples, and classic papers. The output is an enriched understanding and practical approaches to building effective recommendation engines and ad platforms, ideal for data scientists, machine learning engineers, and product managers in e-commerce or ad tech.

recommendation-systems computational-advertising click-through-rate-prediction conversion-rate-optimization e-commerce-personalization

Scores updated daily from GitHub, PyPI, and npm data. How scores work