The classic Mallows model is a widely-used tool to realize distributions on permutations. 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.
Mallows Models for Top-k Lists / Chierichetti, F; Dasgupta, A; Haddadan, S; Kumar, R; Lattanzi, S. - 31:(2018). (Intervento presentato al convegno NeurIPS tenutosi a Montreal; Canada).
Mallows Models for Top-k Lists
Chierichetti, F;Haddadan, S;
2018
Abstract
The classic Mallows model is a widely-used tool to realize distributions on permutations. 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
