  Wikipedia is edited by volunteer editors around the world. Considering the
large amount of existing content (e.g. over 5M articles in English Wikipedia),
deciding what to edit next can be difficult, both for experienced users that
usually have a huge backlog of articles to prioritize, as well as for newcomers
who that might need guidance in selecting the next article to contribute.
Therefore, helping editors to find relevant articles should improve their
performance and help in the retention of new editors. In this paper, we address
the problem of recommending relevant articles to editors. To do this, we
develop a scalable system on top of Graph Convolutional Networks and Doc2Vec,
learning how to represent Wikipedia articles and deliver personalized
recommendations for editors. We test our model on editors' histories,
predicting their most recent edits based on their prior edits. We outperform
competitive implicit-feedback collaborative-filtering methods such as WMRF
based on ALS, as well as a traditional IR-method such as content-based
filtering based on BM25. All of the data used on this paper is publicly
available, including graph embeddings for Wikipedia articles, and we release
our code to support replication of our experiments. Moreover, we contribute
with a scalable implementation of a state-of-art graph embedding algorithm as
current ones cannot efficiently handle the sheer size of the Wikipedia graph.
