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Clinical Online Recommendation with Subgroup Rank Feedback

Sui, Yanan and Burdick, Joel (2014) Clinical Online Recommendation with Subgroup Rank Feedback. In: RecSys '14 Proceedings of the 8th ACM Conference on Recommender Systems. Association for Computing Machinery , New York, NY, pp. 289-292. ISBN 9781450326681.

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Many real applications in experimental design need to make decisions online. Each decision leads to a stochastic reward with initially unknown distribution. New decisions are made based on the observations of previous rewards. To maximize the total reward, one needs to solve the tradeoff between exploring different strategies and exploiting currently optimal strategies. This kind of tradeoff problems can be formalized as Multi-armed bandit problem. We recommend strategies in series and generate new recommendations based on noisy rewards of previous strategies. When the reward for a strategy is difficult to quantify, classical bandit algorithms are no longer optimal. This paper, studies the Multi-armed bandit problem with feedback given as a stochastic rank list instead of quantified reward values. We propose an algorithm for this new problem and show its optimality. A real application of this algorithm on clinical treatment is helping paralyzed patient to regain the ability to stand on their own feet.

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Additional Information:Copyright is held by the owner/author(s). Publication rights licensed to ACM. This work was supported by the the Helmsley Foundation, the Christopher and Dana Reeve Foundation, and the National Institutes of Health (NIH).
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Helmsley FoundationUNSPECIFIED
Christopher and Dana Reeve FoundationUNSPECIFIED
Subject Keywords:Clinical Recommendation, Exploration-Exploitation Trade-off, Bandit Problem, Rank-Comparison
Record Number:CaltechAUTHORS:20141015-100508890
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:50398
Deposited By: Tony Diaz
Deposited On:09 Mar 2020 14:56
Last Modified:10 Nov 2021 18:55

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