  To foster an active and engaged community, social networks employ
recommendation algorithms that filter large amounts of contents and provide a
user with personalized views of the network. Popular social networks such as
Facebook and Twitter generate follow recommendations by listing profiles a user
may be interested to connect with. Federated social networks aim to resolve
issues associated with the popular social networks - such as large-scale
user-surveillance and the miss-use of user data to manipulate elections - by
decentralizing authority and promoting privacy. Due to their recent emergence,
recommender systems do not exist for federated social networks, yet. To make
these networks more attractive and promote community building, we investigate
how recommendation algorithms can be applied to decentralized social networks.
We present an offline and online evaluation of two recommendation strategies: a
collaborative filtering recommender based on BM25 and a topology-based
recommender using personalized PageRank. Our experiments on a large unbiased
sample of the federated social network Mastodon shows that collaborative
filtering approaches outperform a topology-based approach, whereas both
approaches significantly outperform a random recommender. A subsequent live
user experiment on Mastodon using balanced interleaving shows that the
collaborative filtering recommender performs on par with the topology-based
recommender.
