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.

翻译：在本文中,我们研究了对一组要素附加内容的评估问题。 这个问题是困难的, 因为在一般情况下, 它不能被简化为对两种选择的无条件偏好。 因此, 我们根据决定的背景来模拟偏好。 我们讨论并比较了用于这项任务的两种不同的暹罗网络结构: 将添加后产生的两组元素作比较的双子网络, 以及将每个候选人对现有组的贡献做成模型的三重网络。 我们评估了现实世界任务的两个设置; 在收集卡游戏“ 魔术: 聚会” 中, 学习人牌对甲板建筑的偏好。 我们证明三重方法比双对齐网络取得了更好的结果, 并且两者都比先前在这项工作上取得的结果要好。