用双曲空间建模长尾数据，提升跨域推荐冷启动效果
Hgformer: Hyperbolic Graph Transformer for Recommendation
- 基于双曲流形设计新传播与迁移层
- 在多个数据集上显著优于基线模型
- 适合处理长尾用户物品的跨域推荐
冷启动问题是现代推荐系统面临的挑战。通过引入其他领域的知识，跨域推荐可有效缓解此问题。然而，推荐系统中广泛存在的长尾数据建模失真常被忽视。本文以BiTGCF为基线，提出基于双曲流形的跨域协同过滤模型。引入双曲流形，构建新的传播层与迁移层以解决上述问题。在多个数据集上的显著性能提升证明了所提模型的有效性。
原文摘要 · Abstract (English)
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.
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