A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems

• URL: http://arxiv.org/abs/2107.04438v1
• Date: Fri, 9 Jul 2021 13:33:16 GMT
• Title: A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems
• Abstract summary: We study the problem of evaluating the addition of elements to a set. We model preferences based on the context of the decision. We show that the triplet approach achieves a better result than the twin network and that both outperform previous results on this task.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we study the problem of evaluating the addition of elements to a set. This problem is difficult, because it can, in the general case, not be reduced to unconditional preferences between the choices. Therefore, we model preferences based on the context of the decision. We discuss and compare two different Siamese network architectures for this task: a twin network that compares the two sets resulting after the addition, and a triplet network that models the contribution of each candidate to the existing set. We evaluate the two settings on a real-world task; learning human card preferences for deck building in the collectible card game Magic: The Gathering. We show that the triplet approach achieves a better result than the twin network and that both outperform previous results on this task.

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