Yu Tian, Sebastian Lautz, Alisdiar O. G. Wallis, Renaud Lambiotte
arXiv 1 Mar 2021 · cs.SI · 1 citations (OpenAlex)
arXiv:2103.02042 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | F. Ruiz, S. Athey, and D. Blei, “SHOPPER: a probabilistic model of c… (2020) SHOPPER: a probabilistic model of consumer choice with substitutes and complements | 0.928 | 4 | 3 | 100% |
| 2 | F. Chen, X. Liu, D. Proserpio, I. Troncoso, and F. Xiong, “Studying… (2020) Studying product competition using representation learning | 0.737 | 3 | 2 | 100% |
| 3 | Y. Ahn, S. Ahnert, J. Bagrow, and A. Barabási, “Flavor network and t… (2011) Flavor network and the principles of food pairing | 0.644 | 2 | 2 | 100% |
| 4 | S. Athey and S. Stern, “An empirical framework for testing theories… (1998) An empirical framework for testing theories about complementarity in orgaziational design | 0.644 | 2 | 2 | 100% |
| 5 | L. Le Cam, “An approximation theorem for the poisson binomial distri… (1960) An approximation theorem for the poisson binomial distribution | 0.511 | 3 | 2 | 33% |
| 6 | R. Hastie, T. Tibshirani and J. Friedman, “Unsupervised learning,” i… (2009) Unsupervised learning | 0.511 | 2 | 2 | 50% |
| 7 | M. Newman, “The configuration model,” in Networks, New York: Oxford… (2018) The configuration model | 0.511 | 2 | 2 | 50% |
| 8 | M. Newman, S. Strogatz, and D. Watts, “Random graph with arbitrary d… (2001) Random graph with arbitrary degree distributions and their applications | 0.511 | 2 | 2 | 50% |
| 9 | A. Kök, M. Fisher, and R. Vaidyanathan, “Assortment planning: Review… (2015) Assortment planning: Review of literature and industry practice | 0.511 | 2 | 1 | 100% |
| 10 | T. Zhou, J. Ren, M. Medo, and Y. Zhang, “Bipartite network projectio… (2007) Bipartite network projection and personal recommendation | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 49 scored citations.