This repository is a curated collection of articles and notes on various topics within machine learning and data science, primarily focused on recommendation systems, advertising, and related fields. It serves as a comprehensive learning resource for engineers, researchers, and practitioners interested in these domains, offering insights into practical applications and theoretical advancements.

How It Works

The collection is organized thematically, covering areas such as ranking and CTR estimation, recall matching, user profiling and feature engineering, and computational advertising. It aggregates content from a WeChat public account ("浅梦学习笔记"), providing a structured overview of key concepts, industry practices, and research trends. The content includes detailed explanations of algorithms, case studies from major tech companies, and discussions on emerging technologies.

Quick Start & Requirements

This repository is a collection of articles and does not have a direct installation or execution command. The content covers a wide range of topics, and readers may need to refer to external resources or related projects (e.g., DeepCTR, DeepMatch) for practical implementation.

Highlighted Details

• Extensive coverage of recommendation system components: ranking, recall, user profiling, and feature engineering.
• Deep dives into computational advertising, including OCPC strategies and attribution.
• Exploration of graph algorithms and their applications in recommendation and fraud detection.
• Discussions on NLP and CV techniques, including Transformers and self-supervised learning.

Maintenance & Community

The repository is associated with the "浅梦学习笔记" WeChat public account and a related WeChat contact (deepctrbot). It also links to several related projects like DeepCTR, DeepMatch, DeepCTR-Torch, and GraphEmbedding, indicating an active ecosystem.

Licensing & Compatibility

The repository itself does not specify a license. However, the linked projects (DeepCTR, DeepMatch, etc.) are typically available under permissive licenses like Apache 2.0, allowing for commercial use and integration into closed-source projects.

Limitations & Caveats

This repository is a collection of learning notes and summaries, not a software library. While it provides valuable insights, it does not offer runnable code or direct implementations for all discussed topics. Readers will need to consult the original sources or related projects for practical application.