WebWe study identifying user clusters in contextual multi-armed bandits (MAB). Contextual MAB is an effective tool for many real applications, such as content recommendation and online advertisement. In practice, user dependency plays an essential role in the user’s actions, and thus the rewards. Clustering similar users can improve the quality ... WebAug 31, 2024 · Federated Online Clustering of Bandits. Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems. A line of works, called the clustering of bandits (CLUB), utilize the collaborative effect …
Exploring Clustering of Bandits for Online Recommendation System ...
WebWe focus on studying the federated online clustering of bandit (FCLUB) problem, which aims to minimize the total regret while satisfying privacy and communication considerations. We design a new phase-based scheme for cluster detection and a novel asynchronous … WebJun 21, 2014 · Online clustering of bandits. Pages II-757–II-765. Previous Chapter Next Chapter. ABSTRACT. We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation "bandit") … definition therapeutic effect
Federated Online Clustering of Bandits Request PDF - ResearchGate
WebJul 1, 2024 · The Multi-Armed Bandit (MAB) problem, sometimes called the K -armed bandit problem (Zhao, Xia, Tang and Yin, 2024), is a classic problem in which a fixed limited set of resources (arms) must be selected between competing choices to maximize their expected gain (reward). WebAsynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. University of Virginia: AISTATS: 2024 ... One-Shot Federated Clustering: CMU: ICML: ... Federated Online Learning to Rank with Evolution … WebProceedings of Machine Learning Research definition thief