A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems

dc.contributor.advisor

Banks, David L

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Suresh, Sunith Raj

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2018-03-20T17:59:23Z

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2018-03-20T17:59:23Z

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2017

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Statistical Science

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In this paper, we develop an algorithm that utilizes a Bayesian strategy to determine a sequence of questions to play the 20 Question game. The algorithm is motivated with an application to active recommender systems. We first develop an algorithm that constructs a sequence of questions where each question inquires only about a single binary feature. We test the performance of the algorithm utilizing simulation studies, and find that it performs relatively well under an informed prior. We modify the algorithm to construct a sequence of questions where each question inquires about 2 binary features via AND conjunction. We test the performance of the modified algorithm

via simulation studies, and find that it does not significantly improve performance.

dc.identifier.uri

https://hdl.handle.net/10161/16414

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Statistics

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20 Question Game

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Bayesian

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Machine Learning

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Recommender System

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A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems

dc.type

Master's thesis

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