# Growing Wikipedia Across Languages via Recommendation

arXiv: https://arxiv.org/abs/1604.03235 · HTML: https://mlsanity.com/paper/1604.03235/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2016.

## Built on by (most cited first, 3 of 3)

- [Low-resourced Languages and Online Knowledge Repositories: A Need-Finding Study](https://mlsanity.com/paper/2405.16669/) (2024)
- [Entity Insertion in Multilingual Linked Corpora: The Case of Wikipedia](https://mlsanity.com/paper/2410.04254/) (2024)
- [On the effective transfer of knowledge from English to Hindi Wikipedia](https://mlsanity.com/paper/2412.05708/) (2024)

## Abstract (arXiv)

The different Wikipedia language editions vary dramatically in how comprehensive they are. As a result, most language editions contain only a small fraction of the sum of information that exists across all Wikipedias. In this paper, we present an approach to filling gaps in article coverage across different Wikipedia editions. Our main contribution is an end-to-end system for recommending articles for creation that exist in one language but are missing in another. The system involves identifying missing articles, ranking the missing articles according to their importance, and recommending important missing articles to editors based on their interests. We empirically validate our models in a controlled experiment involving 12,000 French Wikipedia editors. We find that personalizing recommendations increases editor engagement by a factor of two. Moreover, recommending articles increases their chance of being created by a factor of 3.2. Finally, articles created as a result of our recommendations are of comparable quality to organically created articles. Overall, our system leads to more engaged editors and faster growth of Wikipedia with no effect on its quality.