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Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype

arXiv 2505.16918 ↗First posted 22 May 2025cs.LGcited by 0

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Abstract (arXiv)

This paper presents a concise review of Contextual Multi-Armed Bandit (CMAB) methods and introduces an experimental framework for scalable, interpretable offer selection, addressing the challenge of fast-changing offers. The approach models context at the product category level, allowing offers to span multiple categories and enabling knowledge transfer across similar offers. This improves learning efficiency and generalization in dynamic environments. The framework extends standard CMAB methodology to support multi-category contexts, and achieves scalability through efficient feature engineering and modular design. Advanced features such as MPG (Member Purchase Gap) and MF (Matrix Factorization) capture nuanced user-offer interactions, with implementation in Python for practical deployment. A key contribution is interpretability at scale: logistic regression models yield transparent weight vectors, accessible via a large language model (LLM) interface for real-time, user-level tracking and explanation of evolving preferences. This enables the generation of detailed member profiles and identification of behavioral patterns, supporting personalized offer optimization and enhancing trust in automated decisions. By situating our prototype alongside established paradigms like Generalized Linear Models and Thompson Sampling, we demonstrate its value for both research and real-world CMAB applications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 49 references that are in the map. First posted on arXiv 22 May 2025.

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References · 24

  1. 2019Introduction to Online Convex Optimizationcited by 803
  2. 2016Wide & Deep Learning for Recommender Systemscited by 747
  3. 2019Introduction to Multi-Armed Banditscited by 481
  4. 2017A Tutorial on Thompson Samplingcited by 433
  5. 2020Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oraclescited by 197
  6. 2020Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizabilitycited by 115
  7. 2019Weighted Linear Bandits for Non-Stationary Environmentscited by 86
  8. 2018Differentially Private Contextual Linear Banditscited by 82
  9. 2021Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discriminationcited by 53
  10. 2014Resourceful Contextual Banditscited by 45
  11. 2015A Survey on Contextual Multi-armed Banditscited by 34
  12. 2022Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximationcited by 31
12 more
  1. 2021Generalized Linear Bandits with Local Differential Privacycited by 29
  2. 2018PG-TS: Improved Thompson Sampling for Logistic Contextual Banditscited by 19
  3. 2024UCB algorithms for multi-armed bandits: Precise regret and adaptive inferencecited by 19
  4. 2013Generalized Thompson Sampling for Contextual Banditscited by 5
  5. 2024Thompson Sampling in Partially Observable Contextual Banditscited by 5
  6. 2024Linear Contextual Bandits with Hybrid Payoff: Revisitedcited by 5
  7. 2024Partially Observable Contextual Bandits with Linear Payoffscited by 5
  8. 2024Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotionscited by 4
  9. 2024DISCO: An End-to-End Bandit Framework for Personalised Discount Allocationcited by 3
  10. 2015Portrait of an Online Shopper: Understanding and Predicting Consumer Behaviorcited by 2
  11. 2024Contextual Bandit with Herding Effects: Algorithms and Recommendation Applicationscited by 1
  12. 2024Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender Systemcited by 1

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