---
title: "acquire-valued-shoppers-challenge: papers citing it"
description: "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."
url: https://scholar4ai.com/code/kaggle-competition-acquire-valued-shoppers-challenge
markdown_url: https://scholar4ai.com/code/kaggle-competition-acquire-valued-shoppers-challenge.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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. (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)
---

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

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.

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

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

## 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

*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](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 1 |
| [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 1 |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 1 |
| [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Scalable two-phase co-occurring sensitive pattern hiding using MapReduce](https://doi.org/10.1186/s40537-017-0064-9) (Journal Of Big Data, 2017)
  - [MR-OVnTSA: a heuristics based sensitive pattern hiding approach for big data](https://doi.org/10.1007/s10489-020-01749-6) (Applied Intelligence, 2020) — cites the paper above

## Papers

*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](https://doi.org/10.1007/s10462-021-10098-w) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Attia Qammar, Jianguo Ding, Huansheng Ning | 56 | cites it |
| 2017-03-09 | [Scalable two-phase co-occurring sensitive pattern hiding using MapReduce](https://doi.org/10.1186/s40537-017-0064-9) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Shivani Sharma, Durga Toshniwal | 12 | cites it |
| 2020-07-15 | [MR-OVnTSA: a heuristics based sensitive pattern hiding approach for big data](https://doi.org/10.1007/s10489-020-01749-6) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 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](https://doi.org/10.1007/s10115-025-02339-z) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Vishal Kaushal, Sangeeta Sharma | 6 | cites it |
| 2025-06-04 | [Fast yet versatile machine unlearning for deep neural networks](https://doi.org/10.1016/j.neunet.2025.107648) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 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

```bibtex
@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}
}
```

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