Integrated Analytic Methodology Using Visual Image and Meta-Data for Product Recommendation

• Published in: Quantitative bio-science Vol. 41; no. 1; pp. 27 - 35
• Main Authors: YongHak Lee, JaeHoon Oh, SeongMin Yang, SungHwan Kim
• Format: Journal Article
• Language: English
• Published: 계명대학교 자연과학연구소 01.05.2022; 자연과학연구소
• Subjects: 통계학; Visual image information; Meta-data; Recommender system; Text information
• ISSN: 2288-1344; 2508-7185
• DOI: 10.22283/qbs.2022.41.1.27

Because the big data industry holds a significant position nowadays, Meta-Data has been adopted rapidly in various fields. In this paper, we introduce a model that exploits Meta-Data consisting of numerous production data to extract features using a pretrained deep learning model with image and text information. Thus, we can build a relational model between the production data to realize a recommendation system. Regarding the dataset, we determined that combining both image and text data is better than using one type of data to achieve a more accurate prediction from the relational model. Concerning the condition of two mixed languages (English and Korean), the method produces a satisfactory result. According to the relational model of the two products, we can design a recommendation system that suggests products that would be of interest to consumers. KCI Citation Count: 0