  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.
