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.
Cite
@article{arxiv.2502.15693,
title = {Hgformer: Hyperbolic Graph Transformer for Recommendation},
author = {Xin Yang and Xingrun Li and Heng Chang and Jinze Yang and Xihong Yang and Shengyu Tao and Ningkang Chang and Maiko Shigeno and Junfeng Wang and Dawei Yin and Erxue Min},
journal= {arXiv preprint arXiv:2502.15693},
year = {2025}
}