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Multi-Objective Ranked Bandits for Recommender Systems
12 references·1 citing·34 second hop
Multi-Objective Ranked Bandits for Recommender Systems (2017)Multi-Objective Ranked Bandits for Recomm…Ensemble contextual bandits for personalized recommendation (2014)Ensemble contextual bandits f…Ranked bandits in metric spaces: learning diverse rankings over large document collections (2013)Ranked bandits in metric spac…Designing multi-objective multi-armed bandits algorithms: A study (2013)Designing multi-objective mul…Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Regret Analysis of Stochastic…On Bayesian Upper Confidence Bounds for Bandit Problems (2012)On Bayesian Upper Confidence …Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms (2011)Unbiased offline evaluation o…Learning diverse rankings with multi-armed bandits (2008)Learning diverse rankings wit…The long tail : how endless choice is creating unlimited demand (2006)The long tail : how endless c…Prediction, Learning, and Games (2006)Prediction, Learning, and Gam…Proceedings of the International Joint Conference on Neural Networks, 2003. (2003)Proceedings of the Internatio…Finite-time Analysis of the Multiarmed Bandit Problem (2002)Finite-time Analysis of the M…EXTENDING MODERN PORTFOLIO THEORY INTO THE DOMAIN OF CORPORATE DIVERSIFICATION: DOES IT APPLY? (1994)EXTENDING MODERN PORTFOLIO TH…Ensemble Recommendations via Thompson Sampling (2018)Ensemble Recommendations via …A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Online Learning and Online Convex Optimization (2011)Online Learning and Online Convex Optimization (2012)Theory of games and economic behavior (2019)The Nonstochastic Multiarmed Bandit Problem (2002)Contextual Bandits with Similarity Information (2009)Interactively optimizing information retrieval systems as a dueling bandits problem (2009)Almost Optimal Exploration in Multi-Armed Bandits (2013)A Stochastic Approximation Method (1951)Introduction to Online Convex Optimization (2016)Introduction to Online Convex Optimization (2016)Introduction to Multi-Armed Bandits (2019)UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem (2010)Multi-objective Contextual Multi-armed Bandit With a Dominant Objective (2018)Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers (1986)The Cross-Section of Expected Stock Returns (1992)Exploring the determinants of organizational emergence: A legitimacy perspective (2006)Consumer-Based Brand Equity and Firm Risk (2009)Strategic management a stakeholder approach (2010)Corporate Social Responsibility and Firm Value: Disaggregating the Effects on Cash Flow, Risk and Growth (2013)Some aspects of the sequential design of experiments (1952)Optimistic bayesian sampling in contextual-bandit problems (2012)Batch learning from logged bandit feedback through counterfactual risk minimization (2015)Bandit Processes and Dynamic Allocation Indices (1979)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Kullback–Leibler upper confidence bounds for optimal sequential allocation (2013)Learning to Optimize via Information-Directed Sampling (2017)Near-Optimal Regret Bounds for Thompson Sampling (2017)A hybrid ARIMA and support vector machines model in stock price forecasting (2004)Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong (2020)Stock market movement forecast: A Systematic review (2020)Groups Formation and Operations in the Web 2.0 Environment and Social Networks (2008)The long tail of social networking. (2008)Parenting for a Digital Future (2020)
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References

  • 2002Finite-time Analysis of the Multiarmed Bandit Problem5,980 cited
  • 2006Prediction, Learning, and Games3,176 cited
  • 2003Proceedings of the International Joint Conference on Neural Networks, 2003.1,674 cited
  • 2012Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems1,410 cited
  • 2006The long tail : how endless choice is creating unlimited demand552 cited
  • 2008Learning diverse rankings with multi-armed bandits504 cited
  • 2011Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms448 cited
  • 1994EXTENDING MODERN PORTFOLIO THEORY INTO THE DOMAIN OF CORPORATE DIVERSIFICATION: DOES IT APPLY?318 cited
  • 2012On Bayesian Upper Confidence Bounds for Bandit Problems210 cited
  • 2013Designing multi-objective multi-armed bandits algorithms: A study125 cited
  • 2014Ensemble contextual bandits for personalized recommendation98 cited
  • 2013Ranked bandits in metric spaces: learning diverse rankings over large document collections63 cited

Cited by

  • 2018Ensemble Recommendations via Thompson Sampling30 cited

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