Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks

We introduce a simple autoencoder based on hyperbolic geometry for solving standard collaborative filtering problem. In contrast to many modern deep learning techniques, we build our solution using only a single hidden layer. Remarkably, even with such a minimalistic approach, we not only outperform...

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Bibliographic Details
Published in:arXiv.org 2020-08
Main Authors: Mirvakhabova, Leyla, Frolov, Evgeny, Khrulkov, Valentin, Oseledets, Ivan, Tuzhilin, Alexander
Format: Article
Language:English
Subjects:
Euclidean geometry
Geometry
Machine learning
Online Access:Get full text
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Summary:We introduce a simple autoencoder based on hyperbolic geometry for solving standard collaborative filtering problem. In contrast to many modern deep learning techniques, we build our solution using only a single hidden layer. Remarkably, even with such a minimalistic approach, we not only outperform the Euclidean counterpart but also achieve a competitive performance with respect to the current state-of-the-art. We additionally explore the effects of space curvature on the quality of hyperbolic models and propose an efficient data-driven method for estimating its optimal value.
ISSN:2331-8422
DOI:10.48550/arxiv.2008.06716