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Cascading Bandits for Large-Scale Recommendation Problems

arXiv 1603.05359 ↗First posted 17 Mar 2016cs.LG stat.MLcited by 45

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Abstract (arXiv)

Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend K most attractive items from a large set of L candidate items. We propose two algorithms for solving this problem, which are based on the idea of linear generalization. The key idea in our solutions is that we learn a predictor of the attraction probabilities of items from their features, as opposing to learning the attraction probability of each item independently as in the existing work. This results in practical learning algorithms whose regret does not depend on the number of items L. We bound the regret of one algorithm and comprehensively evaluate the other on a range of recommendation problems. The algorithm performs well and outperforms all baselines.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Mar 2016.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 14; 2024: 9; 2025: 7; 2026: 1.

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  1. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  2. 2021Multinomial Logit Contextual Bandits: Provable Optimality and Practicalitycited by 21
  3. 2024Nearly Minimax Optimal Regret for Multinomial Logistic Banditcited by 20
  4. 2022Deep Hierarchy in Banditscited by 18
  5. 2023Contextual Combinatorial Bandits with Probabilistically Triggered Armscited by 18
  6. 2024Optimal Design for Human Preference Elicitationcited by 17
  7. 2023Combinatorial Neural Banditscited by 16
  8. 2023Exponential Smoothing for Off-Policy Learningcited by 15
  9. 2021Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Banditscited by 13
  10. 2023Online Clustering of Bandits with Misspecified User Modelscited by 12
  11. 2022Towards Scalable and Robust Structured Bandits: A Meta-Learning Frameworkcited by 10
  12. 2022Minimax Regret for Cascading Banditscited by 10
33 more
  1. 2025Active Learning for Direct Preference Optimizationcited by 9
  2. 2023Adversarial Attacks on Online Learning to Rank with Click Feedbackcited by 6
  3. 2023Adversarial Attacks on Online Learning to Rank with Stochastic Click Modelscited by 6
  4. 2023Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variancescited by 6
  5. 2023Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendationcited by 6
  6. 2024Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Userscited by 6
  7. 2024Bayesian Off-Policy Evaluation and Learning for Large Action Spacescited by 5
  8. 2023Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewardscited by 4
  9. 2024Cascading Reinforcement Learningcited by 4
  10. 2022Product Ranking for Revenue Maximization with Multiple Purchasescited by 3
  11. 2023Multiplier Bootstrap-based Explorationcited by 3
  12. 2024Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Samplingcited by 3
  13. 2021Contextual Combinatorial Bandits with Changing Action Sets via Gaussian Processescited by 2
  14. 2021On Submodular Contextual Banditscited by 2
  15. 2022Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Modelcited by 2
  16. 2024Neural Combinatorial Clustered Bandits for Recommendation Systemscited by 2
  17. 2025Federated Linear Dueling Banditscited by 2
  18. 2021Cascading Bandit under Differential Privacycited by 1
  19. 2021Online Learning of Independent Cascade Models with Node-level Feedbackcited by 1
  20. 2023Overcoming Prior Misspecification in Online Learning to Rankcited by 1
  21. 2023AdaptEx: A Self-Service Contextual Bandit Platformcited by 1
  22. 2024Misalignment, Learning, and Ranking: Harnessing Users Limited Attentioncited by 1
  23. 2025Multi-User Contextual Cascading Bandits for Personalized Recommendationcited by 1
  24. 2021On Learning to Rank Long Sequences with Contextual Banditscited by 0
  25. 2022UniRank: Unimodal Bandit Algorithm for Online Rankingcited by 0
  26. 2023Exploration of Unranked Items in Safe Online Learning to Re-Rankcited by 0
  27. 2023Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamicscited by 0
  28. 2024Adaptively Learning to Select-Rank in Online Platformscited by 0
  29. 2025MultiScale Contextual Bandits for Long Term Objectivescited by 0
  30. 2025Observation-Free Attacks on Online Learning to Rankcited by 0
  31. 2025Online Learning to Rank under Corruption: A Robust Cascading Bandits Approachcited by 0
  32. 2025Cascading Bandits With Feedbackcited by 0
  33. 2026DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clickscited by 0