[논문]챗봇 기반의 개인화 패션 추천 서비스 향상을 위한 사용자-제품 속성 제안

안효선; 김성훈; 최예림

doi:10.12940/jfb.2023.27.3.50

[국내논문] 챗봇 기반의 개인화 패션 추천 서비스 향상을 위한 사용자-제품 속성 제안
Proposal for User-Product Attributes to Enhance Chatbot-Based Personalized Fashion Recommendation Service 원문보기

패션비즈니스 = Fashion business, v.27 no.3, 2023년, pp.50 - 62

안효선 (이화여자대학교, 의류산업학과) , 김성훈 ((주)에이아이닷엠) , 최예림 (서울여자대학교, 데이터사이언스학과)

Abstract ▼ AI-Helper

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

Keyword

표/그림 (6)

그림 Figure 1. User-Chatbot Conversation in H&M Kik Bot (Husak, et al., 2020, p.892)
표 Table 1. High-Ranked Fashion Items in E-Commerce and Number of Product Reviews
표 Table 2. Composition of User's Upper-Level Attributes and Lower-Level Attributes
표 Table 3. Composition of Product's Upper-Level Attributes and Lower-Level Attributes
표 Table 4. Collecting and Extracting User and Product Attributes in E-Commerce Environments
그림 Figure 2. Multi-Hop Interaction with User-Product Attributes in the Multi-Turn Conversation Process (drawn by authors)

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