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Multi-Objective Ranked Bandits for Recommender Systems · Study

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ArticleMulti-Objective Ranked Bandits for Recommender Systems

Anísio Mendes Lacerda2017

Article2017

Multi-Objective Ranked Bandits for Recommender Systems

Anísio Mendes Lacerda

41 citations·0/0 files stored·Neurocomputing

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Advanced Bandit Algorithms Research Information Retrieval and Search Behavior Recommender Systems and Techniques

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References

2002Finite-time Analysis of the Multiarmed Bandit Problem 2006Prediction, Learning, and Games 2003Proceedings of the International Joint Conference on Neural Networks, 2003.2012Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems 2006The long tail : how endless choice is creating unlimited demand 2008Learning diverse rankings with multi-armed bandits 2011Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms 1994EXTENDING MODERN PORTFOLIO THEORY INTO THE DOMAIN OF CORPORATE DIVERSIFICATION: DOES IT APPLY?

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2018Ensemble Recommendations via Thompson Sampling

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2009Contextual Bandits with Similarity Informationpreprint · 5 shared references 2013Ranked bandits in metric spaces: learning diverse rankings over large document collectionsarticle · 5 shared references 2018Ensemble Recommendations via Thompson Samplingother · 5 shared references 2018A Change-Detection Based Framework for Piecewise-Stationary Multi-Armed Bandit Problemother · 4 shared references 2014Modeling Human Decision Making in Generalized Gaussian Multiarmed Banditsarticle · 4 shared references 2019Bandits and Experts in Metric Spacesarticle · 4 shared references

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Year2017

Typearticle

Published2017-02-10

VenueNeurocomputing

ISSN-L0925-2312

Languageen

Citations41

Files0 public / 0

ShelfAdvanced Bandit Algorithms Research

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doi10.1016/j.neucom.2016.12.076

openalex_workW2587322242

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