# DA-southampton/Tech_Aarticle

主要是我是日常看过的不错的文章的资源汇总，方便自己也分享给大家。有些我看过的，就会做简单的解读，没看过的，就先罗列一下，然后之后看了把解读更新上；涉及到搜索/推荐/自然语言处理。

Repository: https://github.com/DA-southampton/Tech_Aarticle
Canonical: https://ross.abutalabs.com/products/tech_aarticle
License Family: other
Last push: 2021-06-03T11:45:54+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": 2275, "days_push": 1917, "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 1757, forks 324 (observed 2026-08-28T04:05:32.328275+00:00)

## What it is
A curated Chinese-language reading list of technical articles on deep learning applied to search, recommendation, and NLP in industry, with author annotations for articles they have read. It aggregates engineering blog posts from companies like Meituan, Alibaba, JD, and iQiyi covering model deployment, query understanding, and ranking.

## Use cases
- find industry articles on deep learning for search ranking
- learn how companies deploy ML models in production
- study query understanding and intent recognition techniques
- find reading material on recommendation systems
- learn about semantic retrieval and text matching like DSSM
- keep up with applied NLP engineering practices

## When to choose
- you want a curated, annotated list of real-world search/recommendation/NLP engineering articles
- you read Chinese and want industry case studies on applied deep learning
- you are researching how production systems handle model serving and ranking

## When to avoid
- you need runnable code or a software library
- you need English-language resources only
- you need actively maintained content (last updated 2021)

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: search-engine, nlp, machine-learning, documentation
- domain: machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: curated-reading-list, search-and-recommendation, deep-learning-in-production, chinese-content, model-deployment, query-understanding, natural-language-processing, search

## Member repositories
- DA-southampton/Tech_Aarticle (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:32.328275+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 2026-08-30T03:27:38.140132+00:00, confidence not recorded.
  - readme: https://github.com/DA-southampton/Tech_Aarticle (fetched 2026-08-28T04:05:32.328275+00:00, sha 78821c462072)
- Data as of 2026-08-30T08:39:29.467469+00:00.