用图同构网络提升跨市场推荐准确率，解决数据稀疏难题。
Enhancing Cross-Market Recommendation System with Graph Isomorphism Networks: A Novel Approach to Personalized User Experience
- 基于图同构网络建模用户行为，捕捉跨市场用户偏好
- 在NDCG@10和HR@10上优于现有模型，跨市场表现稳定
- 适合新市场或冷启动场景的个性化推荐系统
在全球化商业背景下，跨市场推荐系统（CMRs）对提供跨市场个性化体验至关重要。然而，传统推荐算法难以应对市场特异性与数据稀疏问题，尤其在新兴市场中。本文提出CrossGR模型，利用图同构网络（GINs）增强CMR系统，在NDCG@10和HR@10指标上优于现有基准，展现出对多样化市场段的适应性与准确性。其性能在不同评估时间段保持一致，表明其具备应对市场趋势与用户偏好变化的能力。研究结果表明，GINs为跨市场推荐提供了有前景的方向，推动更智能、个性化且情境感知的全球电商推荐系统发展。
原文摘要 · Abstract (English)
In today's world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algorithms have difficulties dealing with market specificity and data sparsity, especially in new or emerging markets. In this paper, we propose the CrossGR model, which utilizes Graph Isomorphism Networks (GINs) to improve CMR systems. It outperforms existing benchmarks in NDCG@10 and HR@10 metrics, demonstrating its adaptability and accuracy in handling diverse market segments. The CrossGR model is adaptable and accurate, making it well-suited for handling the complexities of cross-market recommendation tasks. Its robustness is demonstrated by consistent performance across different evaluation timeframes, indicating its potential to cater to evolving market trends and user preferences. Our findings suggest that GINs represent a promising direction for CMRs, paving the way for more sophisticated, personalized, and context-aware recommendation systems in the dynamic landscape of global e-commerce.
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