---
title: "avazu-ctr-prediction: papers citing this dataset"
description: "4 papers in the tracked journals link to the Kaggle dataset avazu-ctr-prediction in their Crossref records, the first in 2017; most of them (2) in 2024."
url: https://scholar4ai.com/code/kaggle-competition-avazu-ctr-prediction
markdown_url: https://scholar4ai.com/code/kaggle-competition-avazu-ctr-prediction.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the Kaggle dataset avazu-ctr-prediction in their Crossref records, the first in 2017; most of them (2) in 2024. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# avazu-ctr-prediction: papers linking to it

Research papers in the 480 tracked journals that link to avazu-ctr-prediction (https://www.kaggle.com/competitions/avazu-ctr-prediction), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- Kaggle
- https://www.kaggle.com/competitions/avazu-ctr-prediction

All repositories: https://scholar4ai.com/code.md

## Key findings

- 4 papers in the tracked journals link to the Kaggle dataset avazu-ctr-prediction in their Crossref records, the first in 2017; most of them (2) in 2024.
- By category: AI (3) and Information Systems (1); the journals with the most are Neural Computing and Applications (2), Electronic Commerce Research and Applications (1) and Knowledge and Information Systems (1).

## By year

*Papers linking to avazu-ctr-prediction by publication year*

| Year | Papers |
|---|---:|
| 2017 | 1 |
| 2024 | 2 |
| 2025 | 1 |

## Journals

*Journals whose papers link to avazu-ctr-prediction most*

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| [Electronic Commerce Research and Applications](https://scholar4ai.com/journals/electronic-commerce-research-and-applications.md) | 1 |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 1 |

## Papers

*Papers linking to avazu-ctr-prediction, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2017-11-23 | [A novel methodology for optimizing display advertising campaigns using genetic algorithms](https://doi.org/10.1016/j.elerap.2017.11.004) | [Electronic Commerce Research and Applications](https://scholar4ai.com/journals/electronic-commerce-research-and-applications.md) | Luis Miralles‐Pechuán, Hiram Ponce-Espinosa, Lourdes Martínez-Villaseñor | 41 | cites it |
| 2025-02-13 | [Vertical federated learning: a structured literature review](https://doi.org/10.1007/s10115-025-02356-y) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Afsana Khan, Marijn ten Thij, Anna Wilbik | 40 | cites it |
| 2024-12-07 | [Residual connections improve click-through rate and conversion rate prediction performance](https://doi.org/10.1007/s00521-024-10617-0) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Ergun Biçici | 5 | cites it |
| 2024-08-16 | [DONN: leveraging heterogeneous outer products for CTR prediction](https://doi.org/10.1007/s00521-024-10296-x) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Tae-Suk Kim | 1 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). avazu-ctr-prediction: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-avazu-ctr-prediction

```bibtex
@misc{scholar4ai-code-kaggle-competition-avazu-ctr-prediction,
  author = {{Scholar4AI}},
  title = {avazu-ctr-prediction: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-competition-avazu-ctr-prediction}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).