Whom to Follow on Mastodon?

Recommending Users: Whom to Follow on Federated Social Networks

by Jan Trienes, Andrés Torres Cano, and Djoerd Hiemstra

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.

This paper will be presented at the 17th Dutch-Belgian Information Retrieval workshop in Leiden on 23 November 2018

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Welcome to REDI

Welcome to Research Experiments in Databases and Information Retrieval (REDI)! The theme of this year’s course is: Recommendation in federated social networks. Federated social networks consist of multiple independent servers that cooperate. An example is Mastodon, a free open source implementation of a micro-blogging social network that resembles Twitter. Unlike Twitter (or Facebook for that matter), nobody has a complete view of all accounts and posts in a federated social network. We will address two research problems: 1) How to implement recommendations using only local knowledge of the network? and 2) How to evaluate your system in such a highly dynamic environment?

We are the first University of Twente course with a public Canvas syllabus. Of course, we will appropriately use Mastodon to communicate about REDI. Please make an account on mastodon.utwente.nl and follow the hash tag #REDI. Use the hash tag in questions and toots about the course.