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

arXiv: https://arxiv.org/abs/2107.04438 · HTML: https://mlsanity.com/paper/2107.04438/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [The Many AI Challenges of Hearthstone](https://mlsanity.com/paper/1907.06562/) (2019)
- [AI solutions for drafting in Magic: the Gathering](https://mlsanity.com/paper/2009.00655/) (2020)
- [Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking](https://mlsanity.com/paper/2105.11864/) (2021)

## Built on by (most cited first, 1 of 1)

- [Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability](https://mlsanity.com/paper/2204.09462/) (2022)

## Abstract (arXiv)

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.