Choosing to Rank

Understand

Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict.

• Datasets often exhibit multimodality, intransitivity, or incomplete rankings---particularly when generated by humans---yet popular probabilistic models are often too rigid to capture such complexities.
• In this work we leverage recent progress on similar challenges in discrete choice modeling to form flexible and tractable choice-based models for ranking data.
• We study choice representations, maps from rankings (complete or top-$k$) to collections of choices, as a way of forming ranking models from choice models.