  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.
