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We study the task of maximizing rewards from recommending items (actions) to users sequentially interacting with a recommender system.
Matrix perturbation theory , volume 175
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Finite-time analysis of the multiarmed bandit problem
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Stochastic Linear Optimization under Bandit Feedback
Varsha Dani, Thomas P. Hayes, and Sham M. Kakade · 2008
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Modelling relational data using Bayesian clustered tensor factorization
Ilya Sutskever, Joshua B. Tenenbaum, and Ruslan R Salakhutdinov · 2009
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Linearly parameterized bandits
Paat Rusmevichientong and John N Tsitsiklis · 2010
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Bandits warm-up cold recommender systems
Jérémie Mary, Romaric Gaudel, and Preux Philippe · 2014
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Cold-start problems in recommendation systems via contextual-bandit algorithms
Hai Thanh Nguyen, Jérémie Mary, and Philippe Preux · 2014
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Spectral Bandits for Smooth Graph Functions
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Sequential transfer in multi-armed bandit with finite set of models
Alessandro Lazaric, Emma Brunskill, et al · 2013
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A latent source model for online collaborative filtering
Guy Bresler, George H Chen, and Devavrat Shah · 2014
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A tensor approach to learning mixed membership community models
Animashree Anandkumar, Rong Ge, Daniel Hsu, and Sham M Kakade
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Tensor decompositions for learning latent variable models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, and Matus Telgarsky
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Michal Valko, Rémi Munos, Branislav Kveton, and Tomáš Kocák · 2014
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Spectral methods meet EM: A provably optimal algorithm for crowdsourcing
Yuchen Zhang, Xi Chen, Dengyong Zhou, and Michael I Jordan · 2014
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