Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks

• URL: http://arxiv.org/abs/2008.06716v1
• Date: Sat, 15 Aug 2020 13:21:10 GMT
• Title: Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks
• Authors: Leyla Mirvakhabova, Evgeny Frolov, Valentin Khrulkov, Ivan Oseledets, Alexander Tuzhilin
• Abstract 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.
• Score: 72.62702932371148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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