Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks

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.

Accepted at ACM RecSys 2020; 7 pages

References in corpus (3)

• Hyperbolic Geometry of Complex Networks
• RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
• Poincaré Embeddings for Learning Hierarchical Representations

Cited by in corpus (1)

• Personality-Driven Social Multimedia Content Recommendation