Workload-driven learning of mallows mixtures with pairwise preference data

Abstract

In this paper we present a framework for learning mixtures of Mallows models from large samples of incomplete preferences. The problem we address is of significant practical importance in social choice, recommender systems, and other domains where it is required to aggregate, or otherwise analyze, preferences of a heterogeneous user base. We improve on… (More)
DOI: 10.1145/2932194.2932202

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Cite this paper

@inproceedings{Stoyanovich2016WorkloaddrivenLO, title={Workload-driven learning of mallows mixtures with pairwise preference data}, author={Julia Stoyanovich and Lovro Ilijasic and Haoyue Ping}, booktitle={WebDB}, year={2016} }