mlsanityData to 7 Oct 2026

Latent Contextual Bandits and their Application to Personalized Recommendations for New Users

arXiv 1604.06743 ↗First posted 22 Apr 2016cs.LG cs.AIcited by 18

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Abstract (arXiv)

Personalized recommendations for new users, also known as the cold-start problem, can be formulated as a contextual bandit problem. Existing contextual bandit algorithms generally rely on features alone to capture user variability. Such methods are inefficient in learning new users' interests. In this paper we propose Latent Contextual Bandits. We consider both the benefit of leveraging a set of learned latent user classes for new users, and how we can learn such latent classes from prior users. We show that our approach achieves a better regret bound than existing algorithms. We also demonstrate the benefit of our approach using a large real world dataset and a preliminary user study.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2016.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 4; 2024: 4; 2025: 2; 2026: 2.

18 papers.

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  1. 2021Thompson Sampling with a Mixture Priorcited by 15
  2. 2023Optimal Algorithms for Latent Bandits with Cluster Structurecited by 11
  3. 2021Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Banditscited by 10
  4. 2021Interaction-Grounded Learningcited by 9
  5. 2021Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Supportcited by 7
  6. 2024Partially Observable Contextual Bandits with Linear Payoffscited by 5
  7. 2023Online learning in bandits with predicted contextcited by 4
  8. 2025Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal Transportabilitycited by 4
  9. 2024Non-Stationary Latent Auto-Regressive Banditscited by 3
  10. 2022Information-Gathering in Latent Banditscited by 2
  11. 2024The Importance of Causality in Decision Making: A Perspective on Recommender Systemscited by 1
  12. 2025A Classification View on Meta Learning Banditscited by 1
6 more
  1. 2021The Use of Bandit Algorithms in Intelligent Interactive Recommender Systemscited by 0
  2. 2023Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oraclecited by 0
  3. 2023Adaptive Interventions with User-Defined Goals for Health Behavior Changecited by 0
  4. 2024Identifiable Latent Bandits: Leveraging observational data for personalized decision-makingcited by 0
  5. 2026The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systemscited by 0
  6. 2026CUPID in the Model Zoo: Online Matchmaking for Selecting Your Dream LLMcited by 0