NIPS Proceedings^β

Mallows Models for Top-k Lists

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings

[PDF] [BibTeX] [Supplemental]

Authors

• Flavio Chierichetti
• Anirban Dasgupta
• Shahrzad Haddadan
• Ravi Kumar
• Silvio Lattanzi

Conference Event Type: Poster

Abstract

The classic Mallows model is a widely-used tool to realize distributions on per- mutations. Motivated by common practical situations, in this paper, we generalize Mallows to model distributions on top-k lists by using a suitable distance measure between top-k lists. Unlike many earlier works, our model is both analytically tractable and computationally efficient. We demonstrate this by studying two basic problems in this model, namely, sampling and reconstruction, from both algorithmic and experimental points of view.