To address this issue, we consider a combinatorial bandit problem where the learner selects S actions from a base set of K actions, and displays the results in S (out of M) different positions. The aim is to maximize the cumulative reward or equivalently minimize the regret with respect to...
Though GA have been applied successfully to a wide range of different combinatorial optimization problems, the need for careful and time-consum... U Derigs,M Kabath,M Zils - Springer US 被引量: 15发表: 1998年 A Distributed Multilevel Ant Colonies Approach The paper presents a distributed ...
Bandit-based Large-Neighborhood Search To solve combinatorial optimization problems, MABWiser is integrated intoAdaptive Large Neighborhood Search. The ALNS library enables building metaheuristics for complex optimization problems, whereby MABWiser helps selecting the next best destroy, repair operation (arm)...
We formulate the problem as a novel variant of a contextual combinatorial multi-armed bandit problem. The context takes the form of a probability distribution over the user's latent topic preference, and rewards are a particular nonlinear function of the selected set and the context. These ...
To address these problems, we propose an algorithm named Dynamic Clustering based Contextual Combinatorial Multi-Armed Bandits (DC3MAB), which consists of three configurable key components. Specifically, a dynamic user clustering strategy enables different users in the same cluster to cooperate in ...