Learning RUMs: Reducing Mixture to Single Component via PCA

Understand

We consider the problem of learning a mixture of Random Utility Models (RUMs).

• Despite the success of RUMs in various domains and the versatility of mixture RUMs to capture the heterogeneity in preferences, there has been only limited progress in learning a mixture of RUMs from partial data such as pairwise comparisons.
• In contrast, there have been significant advances in terms of learning a single component RUM using pairwise comparisons.
• In this paper, we aim to bridge this gap between mixture learning and single component learning of RUM by developing a `reduction' procedure.