A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems
| dc.contributor.advisor | Banks, David L | |
| dc.contributor.author | Suresh, Sunith Raj | |
| dc.date.accessioned | 2018-03-20T17:59:23Z | |
| dc.date.available | 2018-03-20T17:59:23Z | |
| dc.date.issued | 2017 | |
| dc.department | Statistical Science | |
| dc.description.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. | |
| dc.identifier.uri | ||
| dc.subject | Statistics | |
| dc.subject | 20 Question Game | |
| dc.subject | Bayesian | |
| dc.subject | Machine learning | |
| dc.subject | Recommender System | |
| dc.title | A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems | |
| dc.type | Master's thesis |
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