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

Page view(s)
3
Checked on Jul 20, 2022
HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
Title:
HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
Other Titles:
WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining
Publication Date:
02 February 2020
Citation:
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.
License type:
PublisherCopyrights
Funding Info:
Description:
Full paper is available from the publisher's website: https://doi.org/10.1145/3336191.3371850
ISBN:

Files uploaded:
Files uploaded:
File Size Format Action
There are no attached files.