Results 41 to 50 of about 492,986 (210)
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
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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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Conditionally Risk-Averse Contextual Bandits [PDF]
, 2023 Contextual bandits with average-case statistical guarantees are inadequate in risk-averse situations because they might trade off degraded worst-case behaviour for better average performance.
Mineiro, Paul +2 more
core
OFFLINE NEURAL CONTEXTUAL BANDITS: PESSIMISM, OPTIMIZATION AND GENERALIZATION
, 2022 OFFLINE NEURAL CONTEXTUAL BANDITS: PESSIMISM, OPTIMIZATION AND ...
T Nguyen-Tang (15300727) +3 more
core
Model selection for contextual bandits
CoRR, 2019 We introduce the problem of model selection for contextual bandits, where a learner must adapt to the complexity of the optimal policy while balancing exploration and exploitation. Our main result is a new model selection guarantee for linear contextual bandits.
Dylan J. Foster +2 more
openaire +4 more sources
Federated Linear Contextual Bandits
CoRR, 2021 This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters. By leveraging the geometric structure of the linear rewards, a collaborative algorithm called Fed-PE is proposed to cope with the heterogeneity across clients without ...
Ruiquan Huang +3 more
openaire +3 more sources
Neural Contextual Bandits for Personalized Recommendation
, 2023 In the dynamic landscape of online businesses, recommender systems are pivotal in enhancing user experiences. While traditional approaches have relied on static supervised learning, the quest for adaptive, user-centric recommendations has led to the ...
He, Jingrui, Ban, Yikun, Qi, Yunzhe
core
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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Stochastic Conservative Contextual Linear Bandits [PDF]
, 2022 Many physical systems have underlying safety considerations that require that the strategy deployed ensures the satisfaction of a set of constraints. Further, often we have only partial information on the state of the system. We study the problem of safe
Ganapathysubramanian, Baskar +5 more
core
Do Retrieval-Trained Embeddings Help Linear Contextual Bandits?
MathematicsText embeddings from retrieval-tuned (dual-encoder) models are increasingly used as context features in contextual bandits for recommendation, on the assumption that an embedding space optimized for inner-product similarity will speed up a linear ...
Mustafa Canim
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