# mJackie/RecSys

计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估

Repository: https://github.com/mJackie/RecSys
Canonical: https://ross.abutalabs.com/products/recsys
License Family: other
Topics: machine-learning, recommender-system, recommendation-system, ctr, ctr-prediction
Last push: 2019-12-17T10:44:07+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": 2735, "days_push": 2451, "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 2081, forks 437 (observed 2026-08-28T04:06:11.614037+00:00)

## What it is
A curated collection of learning resources for recommender systems, computational advertising, and machine learning, with a focus on click-through rate (CTR) and conversion rate (CVR) prediction. It aggregates tutorials, technical articles, tools, code examples, competition write-ups, blogs, and classic papers, primarily in Chinese.

## Use cases
- learning how recommender systems work
- finding resources on CTR prediction models
- studying computational advertising concepts
- preparing for Kaggle CTR prediction competitions
- finding deep learning papers on ad click-through prediction
- comparing statistical and deep learning CTR models

## When to choose
- you want a curated reading list for recommender systems and CTR prediction
- you are preparing for ad-tech or CTR-related machine learning interviews or competitions
- you prefer Chinese-language tutorials and articles

## When to avoid
- you need runnable production code or a maintained library
- you need up-to-date resources, as the repo has not been updated since 2019
- you only read English, since most content is in Chinese

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, nlp
- domain: machine-learning, awesome-lists, tutorials
- platform: cross-platform
- tags: recommender-systems, ctr-prediction, computational-advertising, curated-list, chinese

## Member repositories
- mJackie/RecSys (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:11.614037+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 2026-08-30T02:55:57.884705+00:00, confidence not recorded.
  - readme: https://github.com/mJackie/RecSys (fetched 2026-08-28T04:06:11.614037+00:00, sha a4f03b507249)
- Data as of 2026-08-30T08:39:29.467469+00:00.