recommenders and fun-rec
The Microsoft framework provides production-grade implementation patterns and algorithms for building recommendation systems, while the DataWhale tutorial offers beginner-friendly educational content on recommendation system concepts—making them complementary resources where learners typically progress from the tutorial to the framework.
About recommenders
recommenders-team/recommenders
Best Practices on Recommendation Systems
Provides implementations of classical and deep learning algorithms (ALS, xDeepFM, DKN, sequential models) alongside Jupyter notebooks covering the full recommendation pipeline: data preparation, model training, offline evaluation, hyperparameter optimization, and Azure deployment. Includes utility functions for dataset loading, metric computation, and train/test splitting across multiple backends (CPU, GPU, PySpark), supporting both collaborative filtering and content-based approaches.
About fun-rec
datawhalechina/fun-rec
推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/
Covers the complete technical evolution from traditional cascading architectures (collaborative filtering, vector/sequence retrieval, feature crossing, multi-task/multi-scenario modeling) to generative paradigms (LLM-based generation, diffusion models, chain-of-thought reasoning). Includes production-level system implementation with tokenization strategies, Scaling Law architecture design, end-to-end generative modeling, and hardware-aware optimization techniques across recommendation retrieval, ranking, and reranking stages.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work