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In this paper, we study the problem of evaluating the addition of elements toa set. This problem is difficult, because it can, in the general case, not bereduced to unconditional preferences between the choices. Therefore, we modelpreferences based on the context of the decision. We discuss and compare twodifferent Siamese network architectures for this task: a twin network thatcompares the two sets resulting after the addition, and a triplet network thatmodels the contribution of each candidate to the existing set. We evaluate thetwo settings on a real-world task; learning human card preferences for deckbuilding in the collectible card game Magic: The Gathering. We show that thetriplet approach achieves a better result than the twin network and that bothoutperform previous results on this task.

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