• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.63-70
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• 2022

Fashion is a field in which personal taste acts as the first criterion for purchase, and it is being refined as an important strategy to increase purchase conversion on mobile. Although related studies have been conducted, there are insufficient studies to confirm this according to the detailed purchasing journey of consumers. The purpose of this study is to examine whether the evaluation of user experience factors of personalized recommendation service differs by purchase journey, and to reveal whether it affects purchase intention and satisfaction. Variety, reliability, and convenience showed a significant difference at the level of 0.001% and usefulness at the level of 0.05%. Satisfaction levels were different for each stage, such as novelty and usefulness in the cognitive and interest stage, and high reliability and diversity in the search stage. It has theoretical significance in that it enhances the understanding of the purchase journey by revealing that there is a difference in user evaluation of the personalized recommendation service, and it has practical significance in that it suggests the direction of improvement of the personalized recommendation service strategy. If research on effectiveness is conducted in the future, it will be able to contribute to an advanced strategy.

• Proceedings of the Korea Contents Association Conference
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• 2010.05a
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• pp.342-344
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• 2010

스마트웨어가 사용자 중심으로 다변화 되어가는 유비쿼터스 환경속에서 상황정보를 제공하는 것은 서비스 전략의 중요한 성공요소가 되고 있다. 상황인식은 현실의 상황을 정보화하고 이를 활용하여 지능화된 개인화 서비스를 제공하는 기술이다. 본 논문에서는 상황인식을 이용한 맞춤형 패션 디자인 스타일 추천을 제안하였다. 제안된 방법은 사용자에게 자신의 선호도에 부합하는 패션 디자인 스타일을 제공함으로써 이를 얻기 위한 시간과 비용을 줄여주고, 손쉽게 원하는 스타일에 접근하도록 한다. 상황인식은 개인화 서비스에 필요한 상황정보를 추출하고 분류한다. 그리고 상황인식 기반의 필터링으로 패션 디자인 스타일 추천을 함으로 사용자에게 보다 다양한 서비스를 제공할 수 있다. 이를 사용자 인터페이스로 구축하여 논리적 타당성과 유효성을 검증하기 위해 실험적인 적용을 시도하고자 한다.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.7-17
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• 2021

This study investigated the effect of size recommendation technology convergence on purchase intention mediated by information credibility and satisfaction. The survey for this study was conducted on Amazon Mechanical Turk targeting U. S. residing women aged 18 to 60 years old who have never used size recommendation technology. They experienced the size recommendation technology in the provided web page and returned to the survey to answer the questionnaire. The analysis was done with 213 surveys using SPSS 27.0 and Process Macro (model 6, 5,000 Bootstrapping sample). The dimensions of service quality were found to be responsiveness and ease of use, and both have a significant effect on purchase intention through information credibility and satisfaction.

• Smart Media Journal
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• v.10 no.1
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• pp.116-123
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• 2021

Due to the rapid growth of the online fashion market and the resulting expansion of online choices, there is a problem that the seller cannot directly respond to a large number of consumers individually, although consumers are increasingly demanding for more personalized recommendation services. Images are being tagged as a way to meet consumer's personalization needs, but when people tagging, tagging is very subjective for each person, and artificial intelligence tagging has very limited words and does not meet the needs of users. To solve this problem, we designed an algorithm that recognizes the shape, attribute, and emotional information of the product included in the image with AI, and codes this information to represent all the information that the image has with a combination of codes. Through this algorithm, it became possible by acquiring a variety of information possessed by the image in real time, such as the sensibility of the fashion image and the TPO information expressed by the fashion image, which was not possible until now. Based on this information, it is possible to go beyond the stage of analyzing the tastes of consumers and make hyper-personalized clothing recommendations that combine the tastes of consumers with information about trends and TPOs.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.138-147
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• 2022

Recently, chatbots have been applied in various fields and have shown good results, and many attempts to use chatbots in shopping mall product recommendation services are being conducted on e-commerce platforms. In this paper, for a conversation system that recommends a fashion that a user wants based on conversation between the user and the system and fashion image information, a transformer model that is currently performing well in various AI fields such as natural language processing, voice recognition, and image recognition. We propose a multimodal-based improved transformer model that is improved to increase the accuracy of recommendation by using dialogue (text) and fashion (image) information together for data preprocessing and data representation. We also propose a method to improve accuracy through data improvement by analyzing the data. The proposed system has a recommendation accuracy score of 0.6563 WKT (Weighted Kendall's tau), which significantly improved the existing system's 0.3372 WKT by 0.3191 WKT or more.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.673-674
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• 2020

의식주 중에서 자신을 표현하고 외부와의 교류를 할 수 있는 분야는 패션분야로서 인간 생활과 밀접한 관계를 가지고 있으며 사람들의 개인화된 성향 변화 및 인터넷 환경의 개선으로 트렌드는 빠르게 변화하고 있다. 인공지능 기술의 발전은 단순히 객체의 검출 및 분류에서 벗어나 패션 아이템의 분석 및 세부적인 속성을 분석할 수 있는 수준에 다다랐으며 인공지능 기술을 활용하여 사용자에게 추천할 수 있는 서비스가 출시되고 있다. 패션 트렌드의 빠른 변화 및 인공지능 기술의 발전으로 이를 활용한 플랫폼에 기반을 두어 디자이너에게는 디자인 기술을 향상시킬 수 있으며 사용자에게는 개인화된 제품을 구매할 수 있는 플랫폼 개발이 요구되고 있다. 본 논문에서는 인공지능 기술 기반 패션 분석 기술 개발을 위하여 패션 검출 모듈, 패션 검색 모듈, 패션 검색을 위한 벡터 검색 모듈, 상하의 분리를 위한 세그먼테이션 모듈, 패션 복종 분류 모듈을 개발하여 통합하였으며 패션 검색 정확도는 Top-5 기준 75.28%, 벡터 검색 속도는 벡터당 0.002m sec 이하, 세그먼테이션 추출 정확도 87.6%이상, 패션 검출 결과 IoU 0.5 환경에서 96.2%, 복종분석 90.54%의 성능을 보였다.

• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

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.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.375-381
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• 2019

Recently, the recommendation system has attracted the attention of users as customized recommendation services have been provided focusing on fashion, video and music. But these services are difficult to provide users with proper service according to many different contexts because they do not use contextual information emerging in real time. When applied contextual information expands dimensions, it also increases data sparsity and makes it impossible to recommend proper music for users. Trying to solve these problems, our study proposed a music recommendation system to recommend proper music in real time by applying association rules and using relationships and rules about the current location and time information of users. The accuracy of the recommendation system was measured according to location and time information through 5-fold cross validation. As a result, it was found that the accuracy of the recommendation system was improved as contextual information accumulated.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.37-40
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• 2020

날씨에 대한 인류의 관심은 인류 역사가 시작되면서 지금까지 예측하며 관심 영역인 만큼 인류에게 끼치는 영향이 크다. 초기 인류에게 있어서 의류는 생존을 위한 생존 도구에서 현재는 패션의 영역으로 자기를 표출하거나 자신에게 가장 어울리는 옷을 찾기 위한 욕구로 발전해 왔다. 따라서 본 논문에서는 날씨에 따른 개인의 체감온도와 해당 날씨에 가장 선호하는 의상을 분석하고, 예측하며 추천해주는 시스템을 제안한다. 제안하는 시스템은 지속적인 유지 관리를 통해 보완해 나간다면 날씨와 패션 분야에서 다양한 접목을 하는 등 기술발전을 할 것으로 기대된다.