인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
Customers who chooseproducts on EC sites refer to product attributes, images, and descriptions. The impressions given by images are critical because visual information has a significant impact on customers’ purchase decisions. It is, therefore, important to analyze the relationship between customers’ needs and product images, so several studies to analyze product images from product attributes, invoking deep learning models, have been reported recently. For ex- ample, the method can analyze product images based on customers’ and products’ attributes. However, the abstract needs of customers are not always reflected by only product attributes. On the other hand, some words in product de- scriptions express abstract images of products to express customers’ abstract needs. Therefore, if we can model the relationship between each product by using text information, such as product descriptions, and image information, such as product images, it is possible to capture the product line-up from a higher perspective. In this study, we pro- pose a product image analysis method that uses latent topics extracted from product descriptions using Latent Dirichlet Allocation as conditions of Conditional Variational Autoencoder. The topics reflect product image related to customers’ needs. Applying the proposed method to the actual dataset, we show that it enables capturing product features based on abstract information extracted from product descriptions, more than just considering each feature separately
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-26-02-094587090
