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http://dx.doi.org/10.5392/JKCA.2019.19.03.171

Personalized Recommendation Considering Item Confidence in E-Commerce  

Choi, Do-Jin (충북대학교 정보통신공학과)
Park, Jae-Yeol (충북대학교 정보통신공학과)
Park, Soo-Bin (충북대학교 빅데이터협동과정)
Lim, Jong-Tae (충북대학교 정보통신공학과)
Song, Je-O ((주) 제오시스 기업부설연구소)
Bok, Kyoung-Soo (충북대학교 정보통신공학과)
Yoo, Jae-Soo (충북대학교 정보통신공학과)
Publication Information
The Journal of the Korea Contents Association / v.19, no.3, 2019 , pp. 171-182 More about this Journal
Abstract
As online shopping malls continue to grow in popularity, various chances of consumption are provided to customers. Customers decide the purchase by exploiting information provided by shopping malls such as the reviews of actual purchasing users, the detailed information of items, and so on. It is required to provide objective and reliable information because customers have to decide on their own whether the massive information is credible. In this paper, we propose a personalized recommendation method considering an item confidence to recommend reliable items. The proposed method determines user preferences based on various behaviors for personalized recommendation. We also propose an user preference measurement that considers time weights to apply the latest propensity to consume. Finally, we predict the preference score of items that have not been used or purchased before, and we recommend items that have highest scores in terms of both the predicted preference score and the item confidence score.
Keywords
Online Shopping Malls; Item Confidence; Personalized Recommendation; Collaborative Filtering; Social Network Service;
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