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Conference Paper: Product Clustering Analysis Based on the Retail Product Knowledge Graph

TitleProduct Clustering Analysis Based on the Retail Product Knowledge Graph
Authors
KeywordsClustering
Retail product knowledge graph
Issue Date2021
Citation
Communications in Computer and Information Science, 2021, v. 1505 CCIS, p. 37-40 How to Cite?
AbstractProduct clustering analysis is essential in designing retail marketing strategies. It is a common practice that retailers use to effectively manage their product inventory, marketing promotions, etc. The most intuitive way of clustering products is by their explicit attributes, such as brand, size, and flavor. However, these approaches do not integrate the customer-product interactions, thus ignore the implicit product attributes. In this work, we construct a retail product knowledge graph based on Amazon product metadata. Leveraging a state-of-the-art network embedding method, RotatE, our main objective is to unveil hidden interactions of products by including implicit product attributes. These hidden interactions bring insights to downstream operations such as demand forecasting, production planning, assortment optimization, etc.
Persistent Identifierhttp://hdl.handle.net/10722/330749
ISSN
2023 SCImago Journal Rankings: 0.203
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYe, Yang-
dc.contributor.authorZhang, Qingpeng-
dc.date.accessioned2023-09-05T12:13:51Z-
dc.date.available2023-09-05T12:13:51Z-
dc.date.issued2021-
dc.identifier.citationCommunications in Computer and Information Science, 2021, v. 1505 CCIS, p. 37-40-
dc.identifier.issn1865-0929-
dc.identifier.urihttp://hdl.handle.net/10722/330749-
dc.description.abstractProduct clustering analysis is essential in designing retail marketing strategies. It is a common practice that retailers use to effectively manage their product inventory, marketing promotions, etc. The most intuitive way of clustering products is by their explicit attributes, such as brand, size, and flavor. However, these approaches do not integrate the customer-product interactions, thus ignore the implicit product attributes. In this work, we construct a retail product knowledge graph based on Amazon product metadata. Leveraging a state-of-the-art network embedding method, RotatE, our main objective is to unveil hidden interactions of products by including implicit product attributes. These hidden interactions bring insights to downstream operations such as demand forecasting, production planning, assortment optimization, etc.-
dc.languageeng-
dc.relation.ispartofCommunications in Computer and Information Science-
dc.subjectClustering-
dc.subjectRetail product knowledge graph-
dc.titleProduct Clustering Analysis Based on the Retail Product Knowledge Graph-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-981-16-8143-1_4-
dc.identifier.scopuseid_2-s2.0-85121856271-
dc.identifier.volume1505 CCIS-
dc.identifier.spage37-
dc.identifier.epage40-
dc.identifier.eissn1865-0937-
dc.identifier.isiWOS:000781784900004-

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