# wangshusen/RecommenderSystem

Repository: https://github.com/wangshusen/RecommenderSystem
Canonical: https://ross.abutalabs.com/products/recommendersystem
License Family: other
Last push: 2024-02-07T11:55:31+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1603, "days_push": 938, "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 4174, forks 537 (observed 2026-08-28T04:08:38.126505+00:00)

## What it is
A collection of lecture slides, notes, and videos teaching industrial recommender systems, covering retrieval (ItemCF, UserCF, Swing, two-tower models), ranking, and A/B testing. It is an educational resource rather than runnable software.

## Use cases
- learn how industrial recommender systems work
- understand collaborative filtering algorithms like ItemCF and UserCF
- study two-tower retrieval models and their training
- learn about A/B testing for recommendation systems
- prepare for machine learning engineer interviews in recommendation
- find course slides and videos on deep retrieval

## When to choose
- you want structured course material on recommender system pipelines
- you prefer video lectures with accompanying slides
- you want industry-oriented explanations rather than academic papers

## When to avoid
- you need runnable recommender system code or a library
- you need an up-to-date maintained project with a license
- you need content in English

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, search-engine, data-science
- domain: machine-learning, tutorials, education, big-data
- platform: cross-platform
- tags: recommender-systems, collaborative-filtering, two-tower-model, course-materials, chinese-language

## Member repositories
- wangshusen/RecommenderSystem (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:38.126505+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 2026-08-29T18:22:43.744158+00:00, confidence not recorded.
  - readme: https://github.com/wangshusen/RecommenderSystem (fetched 2026-08-28T04:08:38.126505+00:00, sha 8aefa1152a74)
- Data as of 2026-08-30T08:39:29.467469+00:00.