A digital twin-based comparative reinforcement learning framework for personalized behavioral recommendation [PDF]
Frontiers in Artificial IntelligencePromoting healthy lifestyle behaviors such as physical activity, sleep, diet quality, stress management, hydration, and healthy habits requires adaptive systems capable of responding dynamically to changing behavioral and environmental conditions ...
Ayan Chatterjee, Nurilla Avazov
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Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User's Stream With a Contextual Multi-Armed Bandit [PDF]
Frontiers in Neuroscience, 2022 Ecological Momentary Assessments (EMA) deliver insights on how patients perceive tinnitus at different times and how they are affected by it. Moving to the next level, an mHealth app can support users more directly by predicting a user's next EMA and ...
Saijal Shahania +7 more
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Designing digital health interventions with causal inference and multi-armed bandits: a review [PDF]
Frontiers in Digital HealthRecent statistics from the World Health Organization show that non-communicable diseases account for 74% of global fatalities, with lifestyle playing a pivotal role in their development. Promoting healthier behaviors and targeting modifiable risk factors
Radoslava Švihrová +7 more
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Context-aware Multi-stakeholder Recommender Systems
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Traditional recommender systems help users find the most relevant products or services to match their needs and preferences. However, they overlook the preferences of other sides of the market (aka stakeholders) involved in the system.
Tahereh Arabghalizi +1 more
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Battery-aided privacy preservation of household electricity load profiles under time-of-use tariffs
Energy Reports, 2022 Collecting fine-grained data on electricity usage can reduce the user’s energy cost by accurate demand response and improve the services from utilities. However, non-intrusive power load monitoring (NILM) technology shows that these data will reveal the ...
Mao Zhu, Xi Luo, Zhiyi Li, Can Wan
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Enhancing Dynamic Movie Recommendations With User Expectation Ratings in Contextual Bandit Models [PDF]
ITM Web of ConferencesModern movie recommendation systems face challenges such as dynamic personalization and real-time adaptability. Traditional methods like collaborative filtering and content-based recommendations struggle with dynamic user preferences and cold-start ...
Sun Weiye
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EvoClusterBandit: Adaptive Partitioned Bandit Algorithm for Dynamic Environments with Latent Variable Modeling [PDF]
ITM Web of ConferencesExisting multi-armed bandit algorithms struggle in dynamic environments with latent shifts such as abrupt changes in user behavior or contextual features.
Ren Hedong
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Adaptive Noise Exploration for Neural Contextual Multi-Armed Bandits
AlgorithmsIn contextual multi-armed bandits, the relationship between contextual information and rewards is typically unknown, complicating the trade-off between exploration and exploitation.
Chi Wang, Lin Shi, Junru Luo
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Multi-Armed Bandits Convergence Acceleration Using Cluster Normalizations
IEEE AccessRecommender systems usually operate in non-stationary environments, so the standard techniques used to create recommendations begin to degrade after some use.
Victoria Strenadko +2 more
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Gaussian Process with Vine Copula-Based Context Modeling for Contextual Multi-Armed Bandits
MathematicsWe propose a novel contextual multi-armed bandit (CMAB) framework that integrates copula-based context generation with Gaussian Process (GP) regression for reward modeling, addressing complex dependency structures and uncertainty in sequential decision ...
Jong-Min Kim
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