Year of Publication

2017

Degree Name

Master of Science (MS)

Document Type

Master's Thesis

College

Engineering

Department

Computer Science

First Advisor

Dr. Judy Goldsmith

Abstract

Conditional preference networks (CP-nets) model user preferences over objects described in terms of values assigned to discrete features, where the preference for one feature may depend on the values of other features. Most existing algorithms for learning CP-nets from the user's choices assume that the user chooses between pairs of objects. However, many real-world applications involve the the user choosing from all combinatorial possibilities or a very large subset. We introduce a CP-net learning algorithm for the latter type of choice, and study its properties formally and empirically.

Digital Object Identifier (DOI)

https://doi.org/10.13023/ETD.2017.470

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