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

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2017

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Abstract

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.

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Suresh, Sunith Raj (2017). A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/16414.

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