Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System

TL;DR

This paper proposes a hyperbolic contrastive learning framework for cross-domain recommendation systems, effectively modeling long-tail data distributions and transferring knowledge across domains using hyperbolic space representations.

Contribution

It introduces a novel hyperbolic contrastive learning framework that embeds users and items in hyperbolic space with adjustable curvatures for improved cross-domain recommendation.

Findings

Hyperbolic representations outperform Euclidean ones in CDR tasks.

The proposed method effectively transfers knowledge between domains.

Hyperbolic manifolds better model long-tail data distributions.

Abstract

Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain, and it has been gaining more attention in recent years. Although there have been notable advancements in this area, most current methods represent users and items in Euclidean space, which is not ideal for handling long-tail distributed data in recommendation systems. Additionally, adding data from other domains can worsen the long-tail characteristics of the entire dataset, making it harder to train CDR models effectively. Recent studies have shown that hyperbolic methods are particularly suitable for modeling long-tail distributions, which has led us to explore hyperbolic representations for users and items in CDR scenarios. However, due to the distinct characteristics of the different domains, applying hyperbolic representation learning to CDR tasks is quite challenging. In this paper, we introduce a new framework called Hyperbolic Contrastive Learning (HCTS), designed to capture the unique features of each domain while enabling efficient knowledge transfer between domains. We achieve this by embedding users and items from each domain separately and mapping them onto distinct hyperbolic manifolds with adjustable curvatures for prediction. To improve the representations of users and items in the target domain, we develop a hyperbolic contrastive learning module for knowledge transfer. Extensive experiments on real-world datasets demonstrate that hyperbolic manifolds are a promising alternative to Euclidean space for CDR tasks.