---
title: "spotify/annoy: papers citing this GitHub repository"
description: "11 papers in the tracked journals link to the GitHub repository spotify/annoy in their Crossref records, the first in 2018; most of them (3) in 2023."
url: https://scholar4ai.com/code/github-spotify-annoy
markdown_url: https://scholar4ai.com/code/github-spotify-annoy.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "11 papers in the tracked journals link to the GitHub repository spotify/annoy in their Crossref records, the first in 2018; most of them (3) in 2023. (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)
---

# spotify/annoy: papers linking to it

Research papers in the 480 tracked journals that link to spotify/annoy (https://github.com/spotify/annoy), 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.

- GitHub
- 14,329 stars
- 1,231 forks
- C++
- license Apache-2.0
- created 2013
- https://github.com/spotify/annoy

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

## Key findings

- 11 papers in the tracked journals link to the GitHub repository spotify/annoy in their Crossref records, the first in 2018; most of them (3) in 2023.
- By category: AI (8), Management (1) and Higher Education and Research (1); the journals with the most are Data Mining and Knowledge Discovery (2), Technological Forecasting and Social Change (1) and Knowledge-Based Systems (1).
- 4 of the papers build on one another: a later paper cites an earlier one that also links spotify/annoy.

## By year

*Papers linking to spotify/annoy by publication year*

| Year | Papers |
|---|---:|
| 2018 | 1 |
| 2019 | 2 |
| 2021 | 1 |
| 2022 | 1 |
| 2023 | 3 |
| 2024 | 1 |
| 2026 | 2 |

## Journals

*Journals whose papers link to spotify/annoy most*

| Journal | Papers |
|---|---:|
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 2 |
| [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | 1 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Scientometrics](https://scholar4ai.com/journals/scientometrics.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Fast Cosine Similarity Search in Binary Space with Angular Multi-Index Hashing](https://doi.org/10.1109/tkde.2018.2828095) (IEEE Transactions on Knowledge and Data Engineering, 2018)
  - [Online Nearest Neighbor Search Using Hamming Weight Trees](https://doi.org/10.1109/tpami.2019.2902391) (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019) — cites the paper above
- [A text-embedding-based approach to measuring patent-to-patent technological similarity](https://doi.org/10.1016/j.techfore.2022.121559) (Technological Forecasting and Social Change, 2022)
  - [A scalable framework for scholarly similarity and novelty measurement with LLM-derived semantic embeddings](https://doi.org/10.1007/s11192-026-05754-3) (Scientometrics, 2026) — cites the paper above

## Papers

*Papers linking to spotify/annoy, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2019-02-06 | [Proximity Forest: an effective and scalable distance-based classifier for time series](https://doi.org/10.1007/s10618-019-00617-3) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Benjamin Lucas, Ahmed Shifaz, Charlotte Pelletier et al. | 171 | cites it |
| 2022-02-10 | [A text-embedding-based approach to measuring patent-to-patent technological similarity](https://doi.org/10.1016/j.techfore.2022.121559) | [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | Daniel Stefan Hain, Roman Jurowetzki, Tobias Olaf Buchmann et al. | 113 | cites it |
| 2023-08-18 | [Medical image retrieval via nearest neighbor search on pre-trained image features](https://doi.org/10.1016/j.knosys.2023.110907) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Deepak Kumar Gupta, Russell F. Loane, Soumya Gayen et al. | 25 | cites it |
| 2018-04-18 | [Fast Cosine Similarity Search in Binary Space with Angular Multi-Index Hashing](https://doi.org/10.1109/tkde.2018.2828095) | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | Sepehr Eghbali, Ladan Tahvildari | 20 | cites it |
| 2021-01-01 | [Optimizing Revenue While Showing Relevant Assortments at Scale](https://doi.org/10.1016/j.ejor.2021.08.006) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Theja Tulabandhula, Deeksha Sinha, Saketh Reddy Karra | 16 | cites it |
| 2019-03-01 | [Online Nearest Neighbor Search Using Hamming Weight Trees](https://doi.org/10.1109/tpami.2019.2902391) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | Sepehr Eghbali, Hassan Ashtiani, Ladan Tahvildari | 14 | cites it |
| 2023-08-27 | [Variable screening for Lasso based on multidimensional indexing](https://doi.org/10.1007/s10618-023-00950-8) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Barbara Żogała-Siudem, Szymon Jaroszewicz | 2 | cites it |
| 2026-03-07 | [TNStream: Applying tightest neighbors to micro-clusters to define multi-density clusters in streaming data](https://doi.org/10.1016/j.eswa.2026.131840) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Qifen Zeng, Haomin Bao, Hu Yuanzhuo et al. | 2 | cites it |
| 2023-05-19 | [Accelerating massive queries of approximate nearest neighbor search on high-dimensional data](https://doi.org/10.1007/s10115-023-01899-2) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Yingfan Liu, Chaowei Song, Hong Cheng et al. | 0 | cites it |
| 2024-11-29 | [Ensemble microbial classification based on space partitioning and data augmentation](https://doi.org/10.1007/s10489-024-05961-6) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Liu-Ying Wen, Zhu Chen, Fan Min | 0 | cites it |
| 2026-07-20 | [A scalable framework for scholarly similarity and novelty measurement with LLM-derived semantic embeddings](https://doi.org/10.1007/s11192-026-05754-3) | [Scientometrics](https://scholar4ai.com/journals/scientometrics.md) | Jin Wang, Yi Ding, Rui Pan | 0 | 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). spotify/annoy: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-spotify-annoy

```bibtex
@misc{scholar4ai-code-github-spotify-annoy,
  author = {{Scholar4AI}},
  title = {spotify/annoy: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-spotify-annoy}},
  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/).