# imsheridan/DeepRec

推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction

Repository: https://github.com/imsheridan/DeepRec
Canonical: https://ross.abutalabs.com/products/imsheridan-deeprec
License: MIT
License Family: permissive
Topics: deep-learning, recommendation-system, recommendation, reinforcement-learning, exploration-exploitation, computational-advertising
Last push: 2024-01-20T06:41:37+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": 2464, "days_push": 956, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1016, forks 216 (observed 2026-08-28T04:03:14.316231+00:00)

## What it is
A curated collection of must-read papers and industry presentations on deep recommendation systems and CTR prediction, organized by topic with a paper structure map. It aggregates classic and cutting-edge research from industry labs like Alibaba, Google, Microsoft, and Huawei.

## Use cases
- find must-read papers on recommendation systems
- learn CTR prediction models like DIN and DCN
- study deep learning approaches to computational advertising
- get an overview of recommender system research landscape
- find industry papers on feature interaction and user interest modeling
- explore reinforcement learning in recommendation

## When to choose
- you want a curated, organized reading list for recommender systems and CTR prediction
- you need links to original industry papers from top conferences like KDD, WWW, and AAAI
- you are preparing for research or interviews in recommendation/advertising

## When to avoid
- you need runnable code or implementations rather than papers
- you want a maintained software library or tool
- you need beginner tutorials rather than research papers

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: recommendation-systems, ctr-prediction, papers, computational-advertising, curated-list

## Member repositories
- imsheridan/DeepRec (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:14.316231+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 2026-08-30T07:11:19.160608+00:00, confidence not recorded.
  - readme: https://github.com/imsheridan/DeepRec (fetched 2026-08-28T04:03:14.316231+00:00, sha 71aa3b3d3596)
- Data as of 2026-08-30T08:39:29.467469+00:00.