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http://dx.doi.org/10.5392/JKCA.2020.20.02.096

Item Trend Analysis Considering Social Network Data in Online Shopping Malls  

Park, Soobin (충북대학교 빅데이터협동과정)
Choi, Dojin (충북대학교 정보통신공학과)
Yoo, Jaesoo (충북대학교 정보통신공학과)
Bok, Kyoungsoo (원광대학교 SW융합학과)
Publication Information
The Journal of the Korea Contents Association / v.20, no.2, 2020 , pp. 96-104 More about this Journal
Abstract
As consumers' consumption activities become more active due to the activation of online shopping malls, companies are conducting item trend analyses to boost sales. The existing item trend analysis methods are analyzed by considering only the activities of users in online shopping mall services, making it difficult to identify trends for new items without purchasing history. In this paper, we propose a trend analysis method that combines data in online shopping mall services and social network data to analyze item trends in users and potential customers in shopping malls. The proposed method uses the user's activity logs for in-service data and utilizes hot topics through word set extraction from social network data set to reflect potential users' interests. Finally, the item trend change is detected over time by utilizing the item index and the number of mentions in the social network. We show the superiority of the proposed method through performance evaluations using social network data.
Keywords
Social Network Service; Trend Analysis; Online Shopping Mall; Hot Topic; Influence Analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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