Reinforcement learning for personalization: A systematic literature review

Floris den Hengst, Eoin Martino Grua, Ali el Hassouni, Mark Hoogendoorn, Izabela Moise (Editor)

Research output: Contribution to JournalReview articleAcademicpeer-review


The major application areas of reinforcement learning (RL) have traditionally been game playing and continuous control. In recent years, however, RL has been increasingly applied in systems that interact with humans. RL can personalize digital systems to make them more relevant to individual users. Challenges in personalization settings may be different from challenges found in traditional application areas of RL. An overview of work that uses RL for personalization, however, is lacking. In this work, we introduce a framework of personalization settings and use it in a systematic literature review. Besides setting, we review solutions and evaluation strategies. Results show that RL has been increasingly applied to personalization problems and realistic evaluations have become more prevalent. RL has become sufficiently robust to apply in contexts that involve humans and the field as a whole is growing. However, it seems not to be maturing: the ratios of studies that include a comparison or a realistic evaluation are not showing upward trends and the vast majority of algorithms are used only once. This review can be used to find related work across domains, provides insights into the state of the field and identifies opportunities for future work.
Original languageEnglish
Pages (from-to)107-147
Number of pages41
JournalData Science
Issue number2
Publication statusPublished - 11 Nov 2020


  • reinforcement learning
  • contextual bandits
  • personalization
  • adaptive systems
  • Recommender System


Dive into the research topics of 'Reinforcement learning for personalization: A systematic literature review'. Together they form a unique fingerprint.

Cite this