Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit

154 citations recorded by the source corpus.

How researchers see this paper

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DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “[39] uses an ensemble of bandits to improve the performance, [40] proposes a parameter-free model, and [50] addresses the time-varying interest of users.”
Artificial intelligence in recommender systems (2020, background): “There is also some work which treats the interactive process between the user and the recommender system as a multi-arm bandit problem [145] and later extended with contextual information [146, 147].”
Federated Recommendation System via Differential Privacy (2020, methodology): “The MAB algorithm is widely used in recommendation systems due to its simplicity and efficiency [7] [8].”
Advances and Challenges in Conversational Recommender Systems: A Survey (2021, methodology): “Multi-Armed Bandits in CRSs As the classic algorithm for E&E trade-offs, MAB-based models can be seamlessly plugged into the online recommendation setting [203, 215], interactive recommendation [214, 176], and CRSs [32, 206, 95].”
Online Learning: A Comprehensive Survey (2018, background): “4 Contextual Bandits Contextual Bandit is a widely used extension of MAB by associating contextual information with each arm (Zeng et al., 2016; Li et al., 2017b).”