datawhalechina/fun-rec resource

Health v2 · maintenance only

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Adoption not part of the score

7295 stars · 1027 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

An open-source Chinese-language textbook (the 'Wheat Book') teaching recommender systems from classic cascade architectures (collaborative filtering, vector/sequence recall, ranking, reranking) to modern generative paradigms (LLM-based, diffusion-based recommendation). Includes a production-grade system project and interview questions, with code in Python and TensorFlow.

Use cases

• learn recommender systems from scratch
• study generative recommendation with LLMs
• prepare for recommendation algorithm engineer interviews
• understand feature crossing and multi-task ranking models
• build a production-level recommendation system project
• learn diffusion models for recommendation

When to choose

• you want a structured, free curriculum covering both classic and cutting-edge recommendation techniques
• you read Chinese and prefer textbook-style learning with hands-on projects
• you are preparing for recommendation algorithm engineering roles

When to avoid

• you need a production-ready recommendation library rather than educational material
• you only read English (though an English README exists, content is primarily Chinese)
• you need stable, versioned content - the project is under active development and changes frequently

Facets

learning-resource · maturity active

machine-learning deep-learning machine-learning deep-learning tutorials education python cross-platform recommender-system chinese-language open-textbook generative-recommendation collaborative-filtering tianchi-competition interview-preparation recommendation

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