---
title: "recommenderlab (R package): papers citing it"
description: "4 papers in the tracked journals link to recommenderlab (R package) in their Crossref records, the first in 2019; most of them (2) in 2020."
url: https://scholar4ai.com/code/cran-recommenderlab
markdown_url: https://scholar4ai.com/code/cran-recommenderlab.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 recommenderlab (R package) in their Crossref records, the first in 2019; most of them (2) in 2020. (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)
---

# recommenderlab (R package): papers linking to it

Research papers in the 480 tracked journals that link to recommenderlab (R package) (https://CRAN.R-project.org/package=recommenderlab), 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.

- CRAN
- https://CRAN.R-project.org/package=recommenderlab

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

## Key findings

- 4 papers in the tracked journals link to recommenderlab (R package) in their Crossref records, the first in 2019; most of them (2) in 2020.
- By category: Finance (2) and Operations Research (2); the journals with the most are Journal of Revenue and Pricing Management (2) and European Journal of Operational Research (2).
- 4 of the papers build on one another: a later paper cites an earlier one that also links recommenderlab (R package).

## By year

*Papers linking to recommenderlab (R package) by publication year*

| Year | Papers |
|---|---:|
| 2019 | 1 |
| 2020 | 2 |
| 2021 | 1 |

## Journals

*Journals whose papers link to recommenderlab (R package) most*

| Journal | Papers |
|---|---:|
| [Journal of Revenue and Pricing Management](https://scholar4ai.com/journals/journal-of-revenue-and-pricing-management.md) | 2 |
| [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | 2 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Evaluating multi-label classifiers and recommender systems in the financial service sector](https://doi.org/10.1016/j.ejor.2019.05.037) (European Journal of Operational Research, 2019)
  - [Recommendation generation using personalized weight of meta-paths in heterogeneous information networks](https://doi.org/10.1016/j.ejor.2020.01.010) (European Journal of Operational Research, 2020) — cites the paper above
- [Random Utility Models with Cardinality Context Effects for Online Subscription Service Platforms](https://doi.org/10.1057/s41272-019-00227-0) (Journal of Revenue and Pricing Management, 2020)
  - [Assortment optimization under cardinality effects and novelty for unequal profit margin items](https://doi.org/10.1057/s41272-020-00279-7) (Journal of Revenue and Pricing Management, 2021) — cites the paper above

## Papers

*Papers linking to recommenderlab (R package), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2019-06-06 | [Evaluating multi-label classifiers and recommender systems in the financial service sector](https://doi.org/10.1016/j.ejor.2019.05.037) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Matthias Bogaert, Justine Lootens, Dirk Van den Poel et al. | 55 | cites it |
| 2020-01-13 | [Recommendation generation using personalized weight of meta-paths in heterogeneous information networks](https://doi.org/10.1016/j.ejor.2020.01.010) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Mukul Gupta, Pradeep Kumar | 51 | cites it |
| 2020-01-08 | [Random Utility Models with Cardinality Context Effects for Online Subscription Service Platforms](https://doi.org/10.1057/s41272-019-00227-0) | [Journal of Revenue and Pricing Management](https://scholar4ai.com/journals/journal-of-revenue-and-pricing-management.md) | Uzma Mushtaque, Jennifer A. Pazour | 6 | cites it |
| 2021-02-12 | [Assortment optimization under cardinality effects and novelty for unequal profit margin items](https://doi.org/10.1057/s41272-020-00279-7) | [Journal of Revenue and Pricing Management](https://scholar4ai.com/journals/journal-of-revenue-and-pricing-management.md) | Uzma Mushtaque, Jennifer A. Pazour | 4 | 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). recommenderlab (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-recommenderlab

```bibtex
@misc{scholar4ai-code-cran-recommenderlab,
  author = {{Scholar4AI}},
  title = {recommenderlab (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-recommenderlab}},
  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/).