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

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Date
2017
Author
Suresh, Sunith Raj
Advisor
Banks, David L
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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.

Type
Master's thesis
Department
Statistical Science
Subject
Statistics
20 Question Game
Bayesian
Machine Learning
Recommender System
Permalink
https://hdl.handle.net/10161/16414
Citation
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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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

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