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Poster
Mallows Models for Top-k Lists
Flavio Chierichetti · Anirban Dasgupta · Shahrzad Haddadan · Ravi Kumar · Silvio Lattanzi

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #133
in Thu Poster Session A »

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.

Keywords: MCMC · Ranking and Preference Learning · Unsupervised Learning

Author Information

Flavio Chierichetti (Sapienza University)
Anirban Dasgupta (IIT Gandhinagar)
Shahrzad Haddadan (Sapienza University, Rome, Italy)
Ravi Kumar (Google)
Silvio Lattanzi (Google Research)

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