avazu-ctr-prediction: papers linking to it

Kaggle · www.kaggle.com/competitions/avazu-ctr-prediction

← Code and data repositories cited in research papers

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.

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#

20172025

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	2
Electronic Commerce Research and Applications	1
Knowledge and Information Systems	1

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	Electronic Commerce Research and Applications	Luis Miralles‐Pechuán, Hiram Ponce-Espinosa, Lourdes Martínez-Villaseñor	41	cites it
2025-02-13	Vertical federated learning: a structured literature review	Knowledge and Information Systems	Afsana Khan, Marijn ten Thij, Anna Wilbik	40	cites it
2024-12-07	Residual connections improve click-through rate and conversion rate prediction performance	Neural Computing and Applications	Ergun Biçici	5	cites it
2024-08-16	DONN: leveraging heterogeneous outer products for CTR prediction	Neural Computing and Applications	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

@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}
}

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