A Generalized Model for Multidimensional Intransitivity
This work addresses the challenge of modeling intransitive relationships in high-dimensional settings, which is incremental as it builds on prior models by generalizing them with additional constraints.
The authors tackled the problem of capturing multifaceted intransitivity in pairwise preference modeling by proposing a probabilistic model that learns d-dimensional player representations and a dataset-specific metric space, which outperforms competing methods in prediction accuracy on real-world datasets like social choice and online games.
Intransitivity is a critical issue in pairwise preference modeling. It refers to the intransitive pairwise preferences between a group of players or objects that potentially form a cyclic preference chain and has been long discussed in social choice theory in the context of the dominance relationship. However, such multifaceted intransitivity between players and the corresponding player representations in high dimensions is difficult to capture. In this paper, we propose a probabilistic model that jointly learns each player's d-dimensional representation (d>1) and a dataset-specific metric space that systematically captures the distance metric in Rd over the embedding space. Interestingly, by imposing additional constraints in the metric space, our proposed model degenerates to former models used in intransitive representation learning. Moreover, we present an extensive quantitative investigation of the vast existence of intransitive relationships between objects in various real-world benchmark datasets. To our knowledge, this investigation is the first of this type. The predictive performance of our proposed method on different real-world datasets, including social choice, election, and online game datasets, shows that our proposed method outperforms several competing methods in terms of prediction accuracy.