ZiyaoGeng/RecLearn

Health v2 · maintenance only

Adoption not part of the score

1986 stars · 490 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

RecLearn is a Python/TensorFlow2.x library implementing a wide collection of recommendation algorithms, covering both matching (Top-K recommendation) and ranking (CTR prediction) stages. It is aimed at students and beginners, with runnable examples on datasets like MovieLens and Amazon.

Use cases

• implement recommender system models in tensorflow 2
• train CTR prediction models like DeepFM and xDeepFM
• learn matching and ranking recommendation algorithms
• run recommendation experiments on MovieLens or Amazon datasets
• study matrix factorization and factorization machine implementations
• prototype sequential recommendation models

When to choose

• you want a beginner-friendly TensorFlow2 codebase for learning recommendation algorithms
• you need reference implementations of classic CTR and matching models
• you want runnable examples on standard recommendation datasets

When to avoid

• you need production-scale, high-performance recommendation serving
• you require PyTorch or non-TensorFlow frameworks
• you need actively maintained code with recent updates

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning data-science education python recommender-system tensorflow2 ctr-prediction collaborative-filtering factorization-machine deepfm matrix-factorization top-k-recommendation learning-resource

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