Personalizing Interventions with Diversity Aware Bandits

Colton Botta, Avi Segal, Kobi Gal

Research output: Contribution to journalConference articlepeer-review


Online systems utilize user data, such as demographics, past performance, preferences and skillset to construct an accurate model of users and maximize personalization. Some of these user features are “shallow” traits which seldom change (e.g. age, race, gender) while others are “deep” traits that are more volatile (e.g. performance, goals, interests). In this work, we explore how reasoning about this diversity of user features can enhance performance of personalized systems. By modeling the personalization process as a Reinforcement Learning (RL) problem, we introduce Diversity Aware Bandits for Intervention Personaliztion (DABIP), a novel contextual multi-armed bandit algorithm that leverages the dynamics within user features to cluster users while maximizing outcomes. We demonstrate the efficacy of this approach using two real world datasets from different domains.

Original languageEnglish
Pages (from-to)254-263
Number of pages10
JournalCEUR Workshop Proceedings
StatePublished - 1 Jan 2023
EventWorkshops at the 2nd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2023 - Munich, Germany
Duration: 26 Jun 202327 Jun 2023


  • Contextual Multi-Armed Bandit
  • Incentives
  • Interventions

ASJC Scopus subject areas

  • Computer Science (all)


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