Multi-output regression improves partial label ranking predictions
MORE-PLR: multi-output regression employed for partial label ranking
Machine Learning
Summary
Sometimes we want a computer to rank options but only have partial or tied preferences available. The authors found a new way to teach computers to handle these incomplete rankings by turning them into a different kind of prediction problem called multi-output regression. They also designed a process to turn these predictions back into helpful ranked groups. Their approach works as well as the best existing methods based on tests.
What this means in practice
- •For recommendation system developers: Generate ranked sets of suggestions even when user preferences are incomplete or contain ties.
- •For market research analysts: Analyze consumer preference data that have tied or partial rankings to better understand product appeal.
Authors
Santo M. A. R. Thies, Juan C. Alfaro, Viktor Bengs
Abstract
The partial label ranking problem is a supervised learning scenario that aims to fit a preference model that predicts a bucket order defined over a set of labels for a given input instance. This problem generalizes the well-known label ranking problem, which, in practice, is limited to outputting total orders of labels. Existing partial label ranking methods have primarily extended label ranking approaches to handle ties in predictions. This paper proposes using multi-output regression to address the partial label ranking problem, introducing an encoder that, during the learning phase, transforms the (possibly incomplete) rankings with ties of labels to multivariate regression targets, an underexplored perspective in both label ranking and partial label ranking. Moreover, during the inference phase, we introduce several post-hoc layers that convert the multi-output regression results into the output bucket order to effectively implement this approach. This framework provides learning strategies that are competitive with the current state-of-the-art partial label ranking methods, as demonstrated through experimental evaluations.