Results 101 to 110 of about 33,931 (272)
Contextual bandits to increase user prediction accuracy in movie recommendation system [PDF]
ITM Web of ConferencesCold-start problems are inevitable phenomena where recommendation systems fail to accurately predict users’ favour and cause the loss of new users. The typical Multi-Armed Bandit (MAB) models are widely adopted as recommendation systems to solve cold ...
Chen Yizhe
doaj +1 more source
Distributed Flow Scheduling in an Unknown Environment [PDF]
, 2012 Flow scheduling tends to be one of the oldest and most stubborn problems in networking. It becomes more crucial in the next generation network, due to fast changing link states and tremendous cost to explore the global structure.
K. Liu +5 more
core
On the Whittle Index for Restless Multi-armed Hidden Markov Bandits
, 2017 We consider a restless multi-armed bandit in which each arm can be in one of two states. When an arm is sampled, the state of the arm is not available to the sampler.
Gopalan, Aditya +2 more
core +1 more source
Absolute, average‐based, and rank‐based aspirations
Strategic Management Journal, Volume 46, Issue 8, Page 1930-1946, August 2025.Abstract Research Summary A key strategic challenge is balancing exploration and exploitation. When individuals' exploration activity is guided by problemistic search, should managers encourage high aspirations? Past work has shown that optimal aspiration (the aspiration level that leads to the highest level of performance in the long run) is lower in ...
Jerker Denrell +3 more
wiley +1 more source
We survey the literature on multi-armed bandit models and their applications in economics. The multi-armed bandit problem is a statistical decision model of an agent trying to optimize his decisions while improving his information at the same time.
Dirk Bergemann, Juuso Valimaki
core
, 2006 We survey the literature on multi-armed bandit models and their applications in economics. The multi-armed bandit problem is a statistical decision model of an agent trying to optimize his decisions while improving his information at the same time.
Bergemann, Dirk, Välimäki, Juuso
core +1 more source
Discrete Choice Multi-Armed Bandits
, 2023 This paper establishes a connection between a category of discrete choice models and the realms of online learning and multiarmed bandit algorithms. Our contributions can be summarized in two key aspects. Firstly, we furnish sublinear regret bounds for a comprehensive family of algorithms, encompassing the Exp3 algorithm as a particular case. Secondly,
Melo, Emerson, Müller, David
openaire +2 more sources
Armed Conflict and Livestock Species Choices in Northern Nigeria: Evidence From Panel Data Analysis
Journal of International Development, Volume 37, Issue 5, Page 1116-1126, July 2025.ABSTRACT This study examines how armed conflict influences livestock species choices among households in northern Nigeria, a region with livelihoods largely dependent on livestock keeping. Using household panel survey data with global georeferenced conflict data, this study observes significant trends in livestock ownership patterns from 2010 to 2016 ...
Olusegun Fadare, Isaac Omorogbe
wiley +1 more source
IEEE AccessThe urgent demand to reduce carbon emissions due to global warming has driven innovative approaches in cloud computing. This paper introduces the Hyper-Heuristic for Cloud Scheduling Problems (HHCSP), a hyper-heuristic designed to optimize tasks in cloud
Vinicius Renan De Carvalho +1 more
doaj +1 more source
Fairness in Federated Learning: Trends, Challenges, and Opportunities
Advanced Intelligent Systems, Volume 7, Issue 6, June 2025.This survey delves into the intricate issues pertinent to fairness in federated learning , where various biasing factors can skew model performance. By systematically analyzing fairness‐aware strategies, evaluation metrics, and future directions, this work identifies pivotal research gaps in existing approaches and sheds light on both challenges and ...
Noorain Mukhtiar +2 more
wiley +1 more source