This repository serves as a comprehensive, curated knowledge base for recommender systems, targeting researchers and practitioners. It systematically organizes seminal papers, surveys, and industrial best practices across the entire recommender system pipeline, from foundational concepts to advanced deep learning and graph-based approaches. The primary benefit is providing a structured overview and entry point into the vast literature and practical considerations of building effective recommendation engines.

How It Works

The repository functions as a detailed index and annotated bibliography of key research papers and technical articles. It categorizes resources by core components of recommender systems, including overview surveys, recall and ranking algorithms, foundational models (NLP/CV), architecture and engineering practices, and industrial solutions. This structure allows users to navigate and understand the evolution and various facets of recommender system development.

Quick Start & Requirements

This repository is a curated collection of academic papers and technical articles, not a runnable software project. Therefore, there are no installation instructions, dependencies, or setup requirements. Users are expected to access and study the referenced papers independently.

Highlighted Details

• Breadth of Coverage: Encompasses foundational recommender system surveys (e.g., content-based, collaborative filtering, hybrid) and extends to cutting-edge deep learning techniques, graph neural networks (GNNs), and sequential modeling.
• Industrial Focus: Features numerous papers detailing real-world implementations and architectures from major tech companies like Google (YouTube), Netflix, Alibaba, Amazon, Facebook, and JD.com.
• Pipeline Stages: Organizes resources logically by the typical stages of a recommender system: overview, recall, pre-ranking, fine-grained ranking (modeling methods, position bias, multi-task, multi-scenario), and re-ranking.
• Algorithm Depth: Provides references to a wide array of algorithms, including factorization machines, deep neural networks (Wide & Deep, DIN, DIEN), tree-based models, embedding techniques (DSSM, node2vec), and transformer-based approaches.

Maintenance & Community

The README does not provide information regarding the maintenance status, update frequency, or community channels (like Discord or Slack) for this repository. It appears to be a static collection of curated links and summaries.

Licensing & Compatibility

No specific open-source license is mentioned for the repository itself. The content consists of references to external academic papers and articles, whose individual licenses would apply. Compatibility for commercial use would depend on the licensing of the referenced external materials.

Limitations & Caveats

The primary limitation is that this is a knowledge repository, not a functional codebase. Users must locate and read the referenced papers to gain insights, and there is no executable code provided for experimentation or direct implementation. The organization, while comprehensive, relies on the user to synthesize information from disparate sources.