GReS：面向供应链平台的图形化跨域推荐
GReS: Graphical Cross-domain Recommendation for Supply Chain Platform
摘要：
供应链平台（SCP）为下游行业提供了大量的原材料。与传统电子商务平台相比，由于用户兴趣有限，SCP中的数据更加稀疏。为了解决数据稀疏性问题，可以应用跨域推荐（CDR），其利用源域信息提高目标域的推荐性能。然而，将CDR直接应用于SCP，忽略了SCP中商品的层次结构，从而降低了推荐性能。为了利用这一特性，本文以餐饮平台为例，提出了一种图形化的跨域推荐模型GReS。该模型首先构建一个树形图来表示菜肴和食材的不同节点的层次结构，然后应用我们提出的结合GCN和BERT模型的Tree2vec方法嵌入图中进行推荐。商业数据集上的实验结果表明，GReS显着优于国家的最先进的方法在跨域推荐供应链平台。
英文摘要：
Supply Chain Platforms (SCPs) provide downstream industries with numerous raw materials. Compared with traditional e-commerce platforms, data in SCPs is more sparse due to limited user interests. To tackle the data sparsity problem, one can apply Cross-Domain Recommendation (CDR) which improves the recommendation performance of the target domain with the source domain information. However, applying CDR to SCPs directly ignores the hierarchical structure of commodities in SCPs, which reduce the recommendation performance. To leverage this feature, in this paper, we take the catering platform as an example and propose GReS, a graphical cross-domain recommendation model. The model first constructs a tree-shaped graph to represent the hierarchy of different nodes of dishes and ingredients, and then applies our proposed Tree2vec method combining GCN and BERT models to embed the graph for recommendations. Experimental results on a commercial dataset show that GReS significantly outperforms state-of-the-art methods in Cross-Domain Recommendation for Supply Chain Platforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
