The complementarity and substitutability between products are essential concepts in retail and marketing. Qualitatively, two products are said to be substitutable if a customer can replace one product by the other, while they are complementary if they tend to be bought together. In this article, we take a network perspective to help automatically identify complements and substitutes from sales transaction data. Starting from a bipartite product-purchase network representation, with both transaction nodes and product nodes, we develop appropriate null models to infer significant relations, either complements or substitutes, between products, and design measures based on random walks to quantify their importance. The resulting unipartite networks between products are then analysed with community detection methods, in order to find groups of similar products for the different types of relationships. The results are validated by combining observations from a real-world basket dataset with the existing product hierarchy, as well as a large-scale flavour compound and recipe dataset.

产品之间的互补性和替代性是零售和营销的基本概念。定性地说，如果客户可以用另一种产品替换一种产品，则称两种产品是可替代的，而如果它们倾向于一起购买，则它们是互补的。在本文中，我们从网络角度帮助自动识别销售交易数据中的互补品和替代品。从双向产品购买网络表示开始，交易节点和产品节点，我们开发了适当的空模型来推断产品之间的重要关系，无论是补充还是替代，并基于随机游走的设计措施来量化它们的重要性。然后使用社区检测方法分析产品之间产生的单方网络，以便为不同类型的关系找到一组相似的产品。通过将来自真实世界篮子数据集的观察结果与现有产品层次结构以及大规模风味化合物和配方数据集相结合，对结果进行了验证。