Hyperbolic Recommender Systems.
Year: 2018
Type: preprint
Abstract: Many well-established recommender systems are based on representationlearning in Euclidean space. In these models, matching functions such as the
Euclidean distance or inner product are typically used for computing similarity
scores between user and item embeddings. This paper investigates the notion of
learning user and item representations in Hyperbolic space. In this paper, we
argue that Hyperbolic space is more suitable for learning user-item embeddings
in the recommendation domain. Unlike Euclidean spaces, Hyperbolic spaces are
intrinsically equipped to handle hierarchical structure, encouraged by its
property of exponentially increasing distances away from origin. We propose
HyperBPR (Hyperbolic Bayesian Personalized Ranking), a conceptually simple but
highly effective model for the task at hand. Our proposed HyperBPR 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 space.
Source: arXiv (Cornell University)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2890645261