# Cascading Bandits for Large-Scale Recommendation Problems

arXiv: https://arxiv.org/abs/1603.05359 · HTML: https://mlsanity.com/paper/1603.05359/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2016.

## Built on by (most cited first, 30 of 45)

- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [Multinomial Logit Contextual Bandits: Provable Optimality and Practicality](https://mlsanity.com/paper/2103.13929/) (2021)
- [Nearly Minimax Optimal Regret for Multinomial Logistic Bandit](https://mlsanity.com/paper/2405.09831/) (2024)
- [Deep Hierarchy in Bandits](https://mlsanity.com/paper/2202.01454/) (2022)
- [Contextual Combinatorial Bandits with Probabilistically Triggered Arms](https://mlsanity.com/paper/2303.17110/) (2023)
- [Optimal Design for Human Preference Elicitation](https://mlsanity.com/paper/2404.13895/) (2024)
- [Combinatorial Neural Bandits](https://mlsanity.com/paper/2306.00242/) (2023)
- [Exponential Smoothing for Off-Policy Learning](https://mlsanity.com/paper/2305.15877/) (2023)
- [Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits](https://mlsanity.com/paper/2110.08627/) (2021)
- [Online Clustering of Bandits with Misspecified User Models](https://mlsanity.com/paper/2310.02717/) (2023)
- [Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework](https://mlsanity.com/paper/2202.13227/) (2022)
- [Minimax Regret for Cascading Bandits](https://mlsanity.com/paper/2203.12577/) (2022)
- [Active Learning for Direct Preference Optimization](https://mlsanity.com/paper/2503.01076/) (2025)
- [Adversarial Attacks on Online Learning to Rank with Click Feedback](https://mlsanity.com/paper/2305.17071/) (2023)
- [Adversarial Attacks on Online Learning to Rank with Stochastic Click Models](https://mlsanity.com/paper/2305.19218/) (2023)
- [Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances](https://mlsanity.com/paper/2306.07549/) (2023)
- [Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation](https://mlsanity.com/paper/2311.15647/) (2023)
- [Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users](https://mlsanity.com/paper/2402.16312/) (2024)
- [Bayesian Off-Policy Evaluation and Learning for Large Action Spaces](https://mlsanity.com/paper/2402.14664/) (2024)
- [Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards](https://mlsanity.com/paper/2306.01121/) (2023)
- [Cascading Reinforcement Learning](https://mlsanity.com/paper/2401.08961/) (2024)
- [Product Ranking for Revenue Maximization with Multiple Purchases](https://mlsanity.com/paper/2210.08268/) (2022)
- [Multiplier Bootstrap-based Exploration](https://mlsanity.com/paper/2302.01543/) (2023)
- [Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling](https://mlsanity.com/paper/2406.03434/) (2024)
- [Contextual Combinatorial Bandits with Changing Action Sets via Gaussian Processes](https://mlsanity.com/paper/2110.02248/) (2021)
- [On Submodular Contextual Bandits](https://mlsanity.com/paper/2112.02165/) (2021)
- [Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Model](https://mlsanity.com/paper/2207.05437/) (2022)
- [Neural Combinatorial Clustered Bandits for Recommendation Systems](https://mlsanity.com/paper/2410.14586/) (2024)
- [Federated Linear Dueling Bandits](https://mlsanity.com/paper/2502.01085/) (2025)
- [Cascading Bandit under Differential Privacy](https://mlsanity.com/paper/2105.11126/) (2021)

## 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.