# zhongqiangwu960812/AI-RecommenderSystem

该仓库尝试整理推荐系统领域的一些经典算法模型

Repository: https://github.com/zhongqiangwu960812/AI-RecommenderSystem
Canonical: https://ross.abutalabs.com/products/ai-recommendersystem
Language: Jupyter Notebook
License Family: other
Last push: 2023-10-15T02:38:04+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2204, "days_push": 1054, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2182, forks 416 (observed 2026-08-28T04:06:23.390732+00:00)

## What it is
A curated collection of classic recommender system algorithm models with code reimplementations in TensorFlow and PyTorch, paired with blog explanations of the underlying papers and principles. It covers the full industrial recommendation pipeline including recall, coarse ranking, fine ranking (CTR models), and re-ranking, demonstrated on public datasets like MovieLens, Amazon, Criteo, and a news recommendation dataset.

## Use cases
- learn classic recommender system algorithms from scratch
- understand CTR prediction models like DeepFM and DIN
- study recall and collaborative filtering techniques
- reimplement recommendation models in TensorFlow or PyTorch
- compare different ranking models on a common dataset
- prepare for recommendation system interviews or competitions
- learn how industrial recommendation funnels work

## When to choose
- you want tutorial-style explanations plus runnable code for classic recommendation models
- you are learning recommender systems and want both theory and implementation
- you want to see model structure clearly in simple TensorFlow/PyTorch versions

## When to avoid
- you need a production-ready recommendation engine or library
- you want a maintained package with an API rather than educational notebooks
- you need guaranteed correctness or a permissive license for commercial use

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, search-engine
- domain: machine-learning, data-science, tutorials
- platform: python
- tags: recommender-system, collaborative-filtering, ctr-prediction, deep-learning-models, tensorflow, pytorch, deepctr, deepmatch, jupyter-notebooks, chinese-language, algorithms

## Member repositories
- zhongqiangwu960812/AI-RecommenderSystem (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:23.390732+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:48:40.465288+00:00, confidence not recorded.
  - readme: https://github.com/zhongqiangwu960812/AI-RecommenderSystem (fetched 2026-08-28T04:06:23.390732+00:00, sha 1ef997987b4d)
- Data as of 2026-08-30T08:39:29.467469+00:00.