In this sense, the current efforts in the recommendation field have concerned about this online environment and modeled their systems as a Multi-Armed Bandit (MAB) problem. Nevertheless, there is not a consensus about the best practices to design, perform, and evaluate the MAB implementations in...
摘要 Federated multi-armed bandits(FMAB)是新的bandit范式,主要灵感来源于cognitive radio 和recommender systems的实际应用场景。这篇论文提出了一个通用型FMAB框架,并研究了该框架下的两种模型。 首先研究了近似模型,在该近似模型中,不同的local model都是global model 的服从于一个未知分布的随机实现。在这个近似模...
multi‐armed banditrecommendation noveltyrecommender systemsRecommender systems are becoming increasingly critical to the success of commerce sales. In spite of their benefits, they suffer from some major challenges including recommendation quality such as the accuracy, diversity, and novelty of ...
Our results show that well-known multi-armed bandit algorithms are extremely effective in sorting customers that are likely to click the e-mail, pointing that we may send about 60\% of the e-mails without observing relevant decrease (i.e. <; 10%) in the number of clicks. 展开 ...
Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards 2023, Advances in Neural Information Processing Systems Thompson Sampling on Asymmetric α-stable Bandits 2023, International Conference on Agents and Artificial Intelligence Modeling Attrition in Recommender Systems with ...
Bandit-based Recommender Systems To solve personalized recommendation problems, MABWiser is integrated into our Mab2Rec library. Mab2Rec enables building content- and context-aware recommender systems, whereby MABWiser helps selecting the next best item (arm). Bandit-based Large-Neighborhood Search To...
In addition to the exploration-exploitation dilemma in the traditional multi-armed bandit problem, we show that the consideration of multiple stages introduces a third component, education, where an agent needs to choose its actions to facilitate the learning of agents in the next stage. To solve...
In this paper we explore the adaptation of a multi-armed bandit approach to achieve this, by representing the combined systems as arms, and the ensemble as a bandit that at each step selects an arm to produce the next round of recommendations. We report experiments showing the effectiveness ...
multi-armed banditcold-startrecommender systemstelecomrate-planRecommending best-fit rate-plans for new users is a challenge for the Telco industry. Rate-plans differ from most traditional products in the way that a user normally only have one product at any give...
The algorithm relies on four main components: a scalarization function, a set of recommendation quality metrics, a dynamic prioritization scheme for weighting these metrics and a base multi-armed bandit algorithm. Results show that our algorithm provides improvements of 7.8 and 10.4% in click-through...