Relational Boosted Bandits

Published in "To appear in the Proceedings of the Thirty-Fifth Annual Conference of the Association for Advancement of Artificial Intelligence (AAAI 2021)"
Ashutosh Kakadiya , Sriraam Natarajan , B Ravindran

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), a contextual 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.