  Contextual bandits algorithms have become essential in real-world user
interaction problems in recent years. However, these algorithms rely on context
as attribute value representation, which makes them unfeasible for real-world
domains like social networks are inherently relational. We propose Relational
Boosted Bandits(RB2), acontextual bandits algorithm for relational domains
based on (relational) boosted trees. RB2 enables us to learn interpretable and
explainable models due to the more descriptive nature of the relational
representation. We empirically demonstrate the effectiveness and
interpretability of RB2 on tasks such as link prediction, relational
classification, and recommendations.
