acquire-valued-shoppers-challenge: papers linking to it

Kaggle · www.kaggle.com/competitions/acquire-valued-shoppers-challenge

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to acquire-valued-shoppers-challenge (https://www.kaggle.com/competitions/acquire-valued-shoppers-challenge), 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#

5 papers in the tracked journals link to the Kaggle dataset acquire-valued-shoppers-challenge in their Crossref records, the first in 2017; most of them (2) in 2025.
By category: AI (5); the journals with the most are Applied Intelligence (1), Neural Networks (1) and Artificial Intelligence Review (1).
2 of the papers build on one another: a later paper cites an earlier one that also links acquire-valued-shoppers-challenge.

By year#

20172025

Papers linking to acquire-valued-shoppers-challenge by publication year
Year	Papers
2017	1
2020	1
2021	1
2025	2

Journals#

Journals whose papers link to acquire-valued-shoppers-challenge most
Journal	Papers
Applied Intelligence	1
Neural Networks	1
Artificial Intelligence Review	1
Knowledge and Information Systems	1
Journal Of Big Data	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Scalable two-phase co-occurring sensitive pattern hiding using MapReduce Journal Of Big Data, 2017
- MR-OVnTSA: a heuristics based sensitive pattern hiding approach for big data Applied Intelligence, 2020 — cites the paper above

Papers linking to acquire-valued-shoppers-challenge, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-11-15	Federated learning attack surface: taxonomy, cyber defences, challenges, and future directions	Artificial Intelligence Review	Attia Qammar, Jianguo Ding, Huansheng Ning	56	cites it
2017-03-09	Scalable two-phase co-occurring sensitive pattern hiding using MapReduce	Journal Of Big Data	Shivani Sharma, Durga Toshniwal	12	cites it
2020-07-15	MR-OVnTSA: a heuristics based sensitive pattern hiding approach for big data	Applied Intelligence	Shivani Sharma, Durga Toshniwal	7	cites it
2025-01-20	Securing the collective intelligence: a comprehensive review of federated learning security attacks and defensive strategies	Knowledge and Information Systems	Vishal Kaushal, Sangeeta Sharma	6	cites it
2025-06-04	Fast yet versatile machine unlearning for deep neural networks	Neural Networks	Kongyang Chen, Dongping Zhang, Bing Mi et al.	3	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). acquire-valued-shoppers-challenge: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-acquire-valued-shoppers-challenge

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

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