HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems

TL;DR

This paper introduces HyperML, a hyperbolic space-based metric learning model for recommender systems, which outperforms Euclidean methods and achieves state-of-the-art results on benchmark datasets.

Contribution

The paper proposes HyperML, a novel hyperbolic metric learning approach that effectively captures complex user-item relationships in recommender systems.

Findings

HyperML outperforms Euclidean-based models in experiments.

HyperML achieves state-of-the-art performance on multiple datasets.

Hyperbolic geometry enhances personalized recommendation accuracy.

Abstract

This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic space and collaborative filtering by exploring Mobius gyrovector spaces where the formalism of the spaces could be utilized to generalize the most common Euclidean vector operations. Overall, this work aims to bridge the gap between Euclidean and hyperbolic geometry in recommender systems through metric learning approach. We propose HyperML (Hyperbolic Metric Learning), a conceptually simple but highly effective model for boosting the performance. Via a series of extensive experiments, we show that our proposed HyperML not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in hyperbolic geometry.