• Corpus ID: 49667363

# Towards Non-Parametric Learning to Rank

@article{Liu2018TowardsNL,
title={Towards Non-Parametric Learning to Rank},
author={Ao Liu and Qiong Wu and Zhenming Liu and Lirong Xia},
journal={ArXiv},
year={2018},
volume={abs/1807.03395}
}
• Published 9 July 2018
• Computer Science
• ArXiv
This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with $n$ agents (users) $\{x_i\}_{i \in [n]}$ and $m$ alternatives (items) $\{y_j\}_{j \in [m]}$, each of which is associated with a latent feature vector. Agents rank items nondeterministically according to the Plackett-Luce model, where the higher the utility of an item to the agent, the more likely this item will be…

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