Hgformer: Hyperbolic Graph Transformer for Recommendation

TL;DR

This paper introduces Hgformer, a hyperbolic graph transformer model designed for recommendation systems, specifically addressing cold start and long-tail data challenges in cross-domain scenarios, showing significant performance gains.

Contribution

The paper proposes a novel hyperbolic manifold-based model with new propagation and transfer layers for improved cross-domain recommendation, addressing long-tail data distortion.

Findings

Significant performance improvements over baseline models across various datasets.

Effective handling of long-tail data distortion in cross-domain recommendation.

Enhanced modeling of cold start problems using hyperbolic geometry.

Abstract

The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effective method to alleviate the cold start problem. However, the modelling distortion for long-tail data, which is widely present in recommender systems, is often overlooked in cross-domain recommendation. In this research, we propose a hyperbolic manifold based cross-domain collaborative filtering model using BiTGCF as the base model. We introduce the hyperbolic manifold and construct new propagation layer and transfer layer to address these challenges. The significant performance improvements across various datasets compared to the baseline models demonstrate the effectiveness of our proposed model.