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From Anticipation to Action: Data Reveal Mobile Shopping Patterns during a Yearly Mega Sale Event in China

DC Field Value Language
dc.contributor.authorGuan, Muzhi-
dc.contributor.authorMeeyoung Cha-
dc.contributor.authorWang, Yue-
dc.contributor.authorLi, Yong-
dc.contributor.authorSun, Jingbo-
dc.date.accessioned2023-01-27T02:57:21Z-
dc.date.available2023-01-27T02:57:21Z-
dc.date.created2022-03-29-
dc.date.issued2022-04-
dc.identifier.issn1041-4347-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/12949-
dc.description.abstract© 1989-2012 IEEE.The online retail market shows a sharp increase in traffic during holiday sales. The ability to distinguish customers who will likely purchase is critical for provisioning traffic and for providing cost-effective promotions. This paper uniquely studies the browsing and purchasing behaviors of online shoppers during a yearly sale event in China, the world's largest online marketplace. Based on 31 million action logs gathered from wide residential areas, we characterize the steps leading to purchases and determine their precursors. We investigate the effect of time (e.g., date, time of date), environment (e.g., platform, viewed category), and action (e.g., session time, clicks, sequence) on purchases. Action cues from shopping behaviors can be used for early detection. While most shoppers start with strong intentions to purchase, yet the moment of ordering comes rather impulsively within 30 seconds to several minutes of browsing. The predictive accuracy reaches as a high AUC of 0.924. The findings in this paper provide an understanding of traffic during mega sale events that can help online shops plan and provide a better user experience for upcoming shopping festivals.-
dc.language영어-
dc.publisherIEEE Computer Society-
dc.titleFrom Anticipation to Action: Data Reveal Mobile Shopping Patterns during a Yearly Mega Sale Event in China-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000766623600020-
dc.identifier.scopusid2-s2.0-85126545702-
dc.identifier.rimsid77931-
dc.contributor.affiliatedAuthorMeeyoung Cha-
dc.identifier.doi10.1109/TKDE.2020.3001558-
dc.identifier.bibliographicCitationIEEE Transactions on Knowledge and Data Engineering, v.34, no.4, pp.1775 - 1787-
dc.relation.isPartOfIEEE Transactions on Knowledge and Data Engineering-
dc.citation.titleIEEE Transactions on Knowledge and Data Engineering-
dc.citation.volume34-
dc.citation.number4-
dc.citation.startPage1775-
dc.citation.endPage1787-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.subject.keywordAuthorOnline shopping festival-
dc.subject.keywordAuthorpurchase prediction-
dc.subject.keywordAuthoruser modeling-
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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