GReS: Graphical Cross-domain Recommendation for Supply Chain Platform
Zhiwen Jing, Ziliang Zhao, Yang Feng, Xaochen Ma, Nan Wu, Shengqiao Kang, Cheng Hong Yang, Yujia Zhang, Hao Guo · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Supply Chain Platforms (SCPs) provide downstream industries with 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) to improve the recommendation performance of the target domain with the source domain information. However, applying CDR to SCPs directly ignores hierarchical structures of commodities in SCPs, which reduce recommendation performance. In this paper, we take the catering platform as an example and propose GReS, a graphical CDR 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 show that GReS significantly outperforms state-of-the-art methods in CDR for SCPs.