TY - GEN
T1 - Product Clustering Analysis Based on the Retail Product Knowledge Graph
AU - Ye, Yang
AU - Zhang, Qingpeng
PY - 2021
Y1 - 2021
N2 - Product 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.
AB - Product 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.
KW - Clustering
KW - Retail product knowledge graph
UR - http://www.scopus.com/inward/record.url?scp=85121856271&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85121856271&origin=recordpage
U2 - 10.1007/978-981-16-8143-1_4
DO - 10.1007/978-981-16-8143-1_4
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 978-981-16-8142-4
T3 - Communications in Computer and Information Science
SP - 37
EP - 40
BT - Web and Big Data. APWeb-WAIM 2021 International Workshops
A2 - Gao, Yunjun
A2 - Liu, An
A2 - Tao, Xiaohui
A2 - Chen, Junying
PB - Springer
CY - Singapore
T2 - 4th International Workshop on Knowledge Graph Management and Applications (KGMA 2021), 3rd International Workshop on Semi-structured Big Data Management and Applications (SemiBDMA 2021), 2nd International Workshop on Deep Learning in Large-scale Unstructured Data Analytics (DeepLUDA 2021) held in conjunction with Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM 2021)
Y2 - 23 August 2021 through 25 August 2021
ER -