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Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning

Author

Listed:
  • Yan Fang

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Jiayin Yu

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Yumei Ding

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Xiaohua Lin

    (School of Economics and Management, Xiamen University of Technology, Xiamen 364021, China)

Abstract

Complementarity and substitutability between products are essential concepts in retail and marketing. To achieve this, existing approaches take advantage of knowledge graphs to learn more evidence for inference. However, they often omit the knowledge that lies in the unstructured data. In this research, we concentrate on inferring complementary and substitutable products in e-commerce from mass structured and unstructured data. An improved knowledge-graph-based reasoning model has been proposed which cannot only derive related products but also provide interpretable paths to explain the relationship. The methodology employed in our study unfolds through several stages. First, a knowledge graph refining entities and relationships from data was constructed. Second, we developed a two-stage knowledge representation learning method to better represent the structured and unstructured knowledge based on TransE and SBERT. Then, the relationship inferring problem was converted into a path reasoning problem under the Markov decision process environment by learning a dynamic policy network. We also applied a soft pruning strategy and a modified reward function to improve the effectiveness of the policy network training. We demonstrate the effectiveness of the proposed method on standard Amazon datasets, and it gives about 5–15% relative improvement over the state-of-the-art models in terms of NDCG@10, Recall@10, Precision @10, and HR@10.

Suggested Citation

  • Yan Fang & Jiayin Yu & Yumei Ding & Xiaohua Lin, 2023. "Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning," Mathematics, MDPI, vol. 11(22), pages 1-29, November.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4709-:d:1284090
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    References listed on IDEAS

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    1. Yu Tian & Sebastian Lautz & Alisdiar O. G. Wallis & Renaud Lambiotte, 2021. "Extracting Complements and Substitutes from Sales Data: A Network Perspective," Papers 2103.02042, arXiv.org, revised Aug 2021.
    2. A. Gürhan Kök & Marshall L. Fisher & Ramnath Vaidyanathan, 2015. "Assortment Planning: Review of Literature and Industry Practice," International Series in Operations Research & Management Science, in: Narendra Agrawal & Stephen A. Smith (ed.), Retail Supply Chain Management, edition 2, chapter 0, pages 175-236, Springer.
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