spotify/annoy: papers linking to it

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

← Code and data repositories cited in research papers

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.

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#

20182026

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	2
Technological Forecasting and Social Change	1
Knowledge-Based Systems	1
Applied Intelligence	1
Scientometrics	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.

Fast Cosine Similarity Search in Binary Space with Angular Multi-Index Hashing IEEE Transactions on Knowledge and Data Engineering, 2018
- Online Nearest Neighbor Search Using Hamming Weight Trees 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 Technological Forecasting and Social Change, 2022
- A scalable framework for scholarly similarity and novelty measurement with LLM-derived semantic embeddings Scientometrics, 2026 — cites the paper above

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	Data Mining and Knowledge Discovery	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	Technological Forecasting and Social Change	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	Knowledge-Based Systems	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	IEEE Transactions on Knowledge and Data Engineering	Sepehr Eghbali, Ladan Tahvildari	20	cites it
2021-01-01	Optimizing Revenue While Showing Relevant Assortments at Scale	European Journal of Operational Research	Theja Tulabandhula, Deeksha Sinha, Saketh Reddy Karra	16	cites it
2019-03-01	Online Nearest Neighbor Search Using Hamming Weight Trees	IEEE Transactions on Pattern Analysis and Machine Intelligence	Sepehr Eghbali, Hassan Ashtiani, Ladan Tahvildari	14	cites it
2023-08-27	Variable screening for Lasso based on multidimensional indexing	Data Mining and Knowledge Discovery	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	Expert Systems with Applications	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	Knowledge and Information Systems	Yingfan Liu, Chaowei Song, Hong Cheng et al.	0	cites it
2024-11-29	Ensemble microbial classification based on space partitioning and data augmentation	Applied Intelligence	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	Scientometrics	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

@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}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).