# wnzhang/rtb-papers

A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.

Repository: https://github.com/wnzhang/rtb-papers
Canonical: https://ross.abutalabs.com/products/rtb-papers
License Family: other
Last push: 2024-12-20T15:58:36+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3928, "days_push": 621, "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 3690, forks 935 (observed 2026-08-28T04:08:14.751511+00:00)

## What it is
A curated collection of research and survey papers on real-time bidding (RTB) based display advertising techniques, maintained by Weinan Zhang. It organizes papers by topic including books, tutorials, surveys, and demand-side platform techniques such as CTR/CVR estimation.

## Use cases
- find research papers on real-time bidding advertising
- survey the state of the art in RTB display advertising
- learn about CTR and CVR prediction techniques
- find tutorials on computational advertising
- research bid optimization methods
- get reading material for ad tech research

## When to choose
- you are a researcher or student studying real-time bidding or computational advertising
- you need a curated bibliography of RTB papers organized by topic
- you want to catch up on survey papers and tutorials in display advertising

## When to avoid
- you need runnable software or code implementations rather than papers
- you need production ad-serving or bidding infrastructure
- you need peer-reviewed literature search beyond what this curated list covers

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: machine-learning, data-science, e-commerce
- platform: -
- tags: awesome-list, papers, real-time-bidding, computational-advertising, display-advertising, ctr-prediction, survey, web-server

## Member repositories
- wnzhang/rtb-papers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:14.751511+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 2026-08-29T18:31:08.150193+00:00, confidence not recorded.
  - readme: https://github.com/wnzhang/rtb-papers (fetched 2026-08-28T04:08:14.751511+00:00, sha ba1f4d2a17f5)
- Data as of 2026-08-30T08:39:29.467469+00:00.