# Relational Boosted Bandits

arXiv: https://arxiv.org/abs/2012.09220 · HTML: https://mlsanity.com/paper/2012.09220/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits](https://mlsanity.com/paper/2106.13895/) (2021)
- [Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals](https://mlsanity.com/paper/2309.09404/) (2023)
- [A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON](https://mlsanity.com/paper/2412.17910/) (2024)
- [The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems](https://mlsanity.com/paper/2604.26651/) (2026)

## Abstract (arXiv)

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.