HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
This addresses personalized recommendation by bridging Euclidean and hyperbolic geometry, showing incremental improvement over existing methods.
The paper tackles the problem of learning user and item representations for recommender systems by proposing HyperML, a metric learning approach in hyperbolic space, which outperforms Euclidean counterparts and achieves state-of-the-art performance on multiple benchmark datasets.
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.