Ensemble contextual bandits for personalized recommendation
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Liang Tang | Tao Li | Lei Li | Yexi Jiang | Tao Li | L. Tang | Lei Li | Yexi Jiang
[1] Yehuda Koren,et al. The BellKor Solution to the Netflix Grand Prize , 2009 .
[2] Joseph Sill,et al. Feature-Weighted Linear Stacking , 2009, ArXiv.
[3] M. Kendall. Probability and Statistical Inference , 1956, Nature.
[4] John Riedl,et al. Meta-recommendation systems: user-controlled integration of diverse recommendations , 2002, CIKM '02.
[5] Wei Chu,et al. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms , 2010, WSDM '11.
[6] Mihaela van der Schaar,et al. Decentralized Online Big Data Classification - a Bandit Framework , 2013, ArXiv.
[7] J. Langford,et al. The Epoch-Greedy algorithm for contextual multi-armed bandits , 2007, NIPS 2007.
[8] Kuan-Wei Wu,et al. A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012 , 2012 .
[9] Peter Auer,et al. The Nonstochastic Multiarmed Bandit Problem , 2002, SIAM J. Comput..
[10] Robert A. Legenstein,et al. Combining predictions for accurate recommender systems , 2010, KDD.
[11] Steven L. Scott,et al. A modern Bayesian look at the multi-armed bandit , 2010 .
[12] Alda Lopes Gançarski,et al. A Contextual-Bandit Algorithm for Mobile Context-Aware Recommender System , 2012, ICONIP.
[13] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[14] P. J. Green,et al. Probability and Statistical Inference , 1978 .
[15] M. Wu,et al. Collaborative Filtering via Ensembles of Matrix Factorizations , 2007, KDD 2007.
[16] Meta Learning in Recommendation Systems , 2013 .
[17] Bianca Zadrozny,et al. Learning and evaluating classifiers under sample selection bias , 2004, ICML.
[18] Marco Tiemann,et al. Towards ensemble learning for hybrid music recommendation , 2007, RecSys '07.
[19] Louis Wehenkel,et al. Meta-learning of Exploration/Exploitation Strategies: The Multi-armed Bandit Case , 2012, ICAART.
[20] W. R. Thompson. ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES , 1933 .
[21] Michèle Sebag,et al. Change Point Detection and Meta-Bandits for Online Learning in Dynamic Environments , 2007 .
[22] Michel Tokic. Adaptive ε-greedy Exploration in Reinforcement Learning Based on Value Differences , 2010 .
[23] Andreas Maurer,et al. Algorithmic Stability and Meta-Learning , 2005, J. Mach. Learn. Res..
[24] David M. Pennock,et al. Categories and Subject Descriptors , 2001 .
[25] Philip K. Chan,et al. Meta-learning in distributed data mining systems: Issues and approaches , 2007 .
[26] Lihong Li,et al. An Empirical Evaluation of Thompson Sampling , 2011, NIPS.
[27] Shou-De Lin,et al. Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation , 2012, KDD Cup.
[28] Guy Shani,et al. Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.
[29] Mehryar Mohri,et al. Multi-armed Bandit Algorithms and Empirical Evaluation , 2005, ECML.
[30] Jürgen Schmidhuber,et al. Algorithm Selection as a Bandit Problem with Unbounded Losses , 2008, LION.
[31] Deepak Agarwal,et al. Online Models for Content Optimization , 2008, NIPS.
[32] R. Polikar,et al. Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.
[33] Wei-Ying Ma,et al. Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes , 2002, UAI.
[34] J MatteoGagliolo. Algorithm Selection as a Bandit Problem with Unbounded Losses , 2008 .
[35] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[36] Christophe G. Giraud-Carrier. Metalearning - A Tutorial , 2008 .
[37] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[38] Wei Chu,et al. A contextual-bandit approach to personalized news article recommendation , 2010, WWW '10.