Extracting complements and substitutes from sales data: a network perspective

Tian, Yu; Lautz, Sebastian; Wallis, Alisdair O. G.; Lambiotte, Renaud

doi:10.1140/epjds/s13688-021-00297-4

Cited by 10 publications

(9 citation statements)

References 47 publications

(59 reference statements)

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“…[1]. For example, retailers determine which product to offer in each category [2]. Brick-andmortar retailers seek to identify the best way to arrange the product layout in aisles and stocking shelves [3], and e-tailers also strive to recommend a list of products or promote more attractive product bundles for consumers [4].…”

Section: Introductionmentioning

confidence: 99%

Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning

Fang,

Yu,

Ding

et al. 2023

Mathematics

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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.

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Section: Introductionmentioning

confidence: 99%

Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning

Fang,

Yu,

Ding

et al. 2023

Mathematics

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“…This observation is important as many phenomena across different application domains, from cooperation, business interactions and division of labor 17 , 20 – 24 , to the quality of romantic relationships 25 , consumer choices 26 and at least some types of protein–protein binding 18 , may indeed be better explained by the principle of complementarity than similarity. For instance, two types of wine may be often bought together with the same kinds of bread and cheese, but rarely both of them will occur in the same transaction.…”

Section: Introductionmentioning

confidence: 99%

Structural measures of similarity and complementarity in complex networks

Talaga

Nowak

2022

Sci Rep

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The principle of similarity, or homophily, is often used to explain patterns observed in complex networks such as transitivity and the abundance of triangles (3-cycles). However, many phenomena from division of labor to protein-protein interactions (PPI) are driven by complementarity (differences and synergy). Here we show that the principle of complementarity is linked to the abundance of quadrangles (4-cycles) and dense bipartite-like subgraphs. We link both principles to their characteristic motifs and introduce two families of coefficients of: (1) structural similarity, which generalize local clustering and closure coefficients and capture the full spectrum of similarity-driven structures; (2) structural complementarity, defined analogously but based on quadrangles instead of triangles. Using multiple social and biological networks, we demonstrate that the coefficients capture structural properties related to meaningful domain-specific phenomena. We show that they allow distinguishing between different kinds of social relations as well as measuring an increasing structural diversity of PPI networks across the tree of life. Our results indicate that some types of relations are better explained by complementarity than homophily, and may be useful for improving existing link prediction methods. We also introduce a Python package implementing efficient algorithms for calculating the proposed coefficients.

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“…Chen et al (2020) follow the previous two approaches and use the complementarity and exchangeability measures based on a low-dimensional space of products. Tian et al (2021) study product relationships in a bipartite product-purchase network and define the substitutability and complementarity measures as cosine similarity.…”

Section: Introductionmentioning

confidence: 99%