# wzhe06/Ad-papers

Papers on Computational Advertising

Repository: https://github.com/wzhe06/Ad-papers
Canonical: https://ross.abutalabs.com/products/ad-papers
Homepage: https://github.com/wzhe06/Ad-papers
Language: Python
License: MIT
License Family: permissive
Topics: computational-advertising, machine-learning, ctr-prediction, deep-learning, advertising, recommender-system, papers
Last push: 2021-02-09T04:23:27+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": 3678, "days_push": 2031, "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 4397, forks 1185 (observed 2026-08-28T04:08:48.223603+00:00)

## What it is
A curated collection of academic papers, tutorials, and industry resources on computational advertising, including CTR prediction, embedding methods, bidding strategies, and optimization. It is a reading list maintained by Wang Zhe rather than runnable software.

## Use cases
- find papers on computational advertising
- learn about CTR prediction models like DIN and DIEN
- study real-time bidding and budget control research
- find machine learning tutorials for ad tech
- research embedding methods for search ranking and recommendations

## When to choose
- you want a curated reading list of computational advertising papers
- you are studying CTR prediction or ad system design
- you need references for recommender systems and ad tech research

## When to avoid
- you need runnable code or a library
- you want actively updated content (last updated 2021)
- you need production advertising tools

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

## Member repositories
- wzhe06/Ad-papers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:48.223603+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 2026-08-29T18:21:10.013097+00:00, confidence not recorded.
  - readme: https://github.com/wzhe06/Ad-papers (fetched 2026-08-28T04:08:48.223603+00:00, sha 964bd4b23f97)
  - homepage: https://github.com/wzhe06/Ad-papers (fetched 2026-08-29T09:09:02.587974+00:00, sha 9ccedbb378e5)
- Data as of 2026-08-30T08:39:29.467469+00:00.