GReS: Graphical Cross-domain Recommendation for Supply Chain Platform
This addresses the data sparsity issue for supply chain platforms, but it is incremental as it builds on existing cross-domain recommendation techniques.
The paper tackled the data sparsity problem in supply chain platforms by proposing GReS, a graphical cross-domain recommendation model that leverages the hierarchical structure of commodities, and it significantly outperformed state-of-the-art methods on a commercial dataset.
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.