• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.447-454
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• 2022

In the era of the 4th industrial revolution, we are living in a flood of information. It is very difficult and complicated to find the information people need in such an environment. Therefore, in the flood of information, a recommendation system is essential. Among these recommendation systems, many studies have been conducted on each recommendation system for movies, music, food, and clothes. To date, most personalized recommendation systems have recommended clothes, books, or movies by checking individual tendencies such as age, genre, region, and gender. Future generations will want to be recommended clothes, books, and movies at once by checking age, genre, region, and gender. In this paper, we propose a recommendation system that recommends personalized clothes and food at once according to the user's emotions and weather. We obtained user data from Twitter of social media and analyzed this data as user's basic emotion according to Paul Eckman's theory. The basic emotions obtained in this way were converted into colors by applying Hayashi's Quantification Method III, and these colors were expressed as recommended clothes colors. Also, the type of clothing is recommended using the weather information of the visualcrossing.com API. In addition, various foods are recommended according to the contents of comfort food according to emotions.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.357-363
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• 2021

In general, people choose clothes to wear when they go out, referring to real-time weather and temperature. However, it is difficult for an individual to use real-time weather information and his or her temperature sensitivity information to choose the right clothes from among the clothes he or she owns. Existing clothing recommendation systems developed to help with these problems have problems recommending clothes that are not clearly set in the clothing category and are not in the possession of the user. In addition, user-specific temperature sensitivity is not taken into account, resulting in inappropriate clothing recommendations for users. To solve these problems, this study developed a system that determines and registers clothing categories for the clothing owned by the user, and recommends customized clothing for each user by considering temperature sensitivity and real-time weather information. In the case of weather information, not only weather information such as temperature and wind direction, but also clothes based on temperature sensitivity were recommended based on the calculation of temperature sensitivities. A satisfaction survey of 65 university students was conducted to assess the system. As a result, 80% of the respondents were satisfied with the recommended clothing, indicating that the satisfaction of the system was good. Therefore, it is expected that this system will be highly utilized in real life as it will be recommended based on clothes owned by individuals, reflecting individual temperature sensitivity.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.688-698
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• 2009

The market for Internet fashion/coordination shopping malls has been enormously increased year by year. However, online shoppers feel inconvenient because most of Internet shopping malls still rely on item classifications by category and do not provide the functionality in terms of which shoppers can find clothes they want. In an effort to build a fashion/coordination system for women's dress adopting the Heuristic-based method, one of the Context-based methods, we present a method for defining characteristics of a woman's dress as attributes and their inheritance relations, which can be input by a product manager. We also compare and analyze various methods for recommending the most similar clothes.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.321-326
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• 2019

As AI technology is recently introduced into various fields, it is being applied to the fashion field. This paper proposes a system for recommending cody clothes suitable for a user's selected clothes. The proposed system consists of user app, cody recommendation module, and server interworking of each module and managing database data. Cody recommendation system classifies clothing images into 80 categories composed of feature combinations, selects multiple representative reference images for each category, and selects 3 full body cordy images for each representative reference image. Cody images of the representative reference image were determined by analyzing the user's preference using Google survey app. The proposed algorithm classifies categories the clothing image selected by the user into a category, recognizes the most similar image among the classification category reference images, and transmits the linked cody images to the user's app. The proposed system uses the ResNet-50 model to categorize the input image and measures similarity using ORB and HOG features to select a reference image in the category. We test the proposed algorithm in the Android app, and the result shows that the recommended system runs well.

• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.1061-1069
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• 2011

With the development of information technology, online commercial transactions have been steadily increasing. However it is not easy to recommend the clothes in coordination with individual physical features. This paper presents a fashion coordination recommender system for women's clothes. The system includes the functionality of recommending clothes best-suited for customers in consideration of their individual physical features. It has also been designed to recommend clothes in vogue for those who are fashion-sensitive by considering the fashion trend of the times. Operated in an optional or coupling way, these functionalities of our system result in an intelligent fashion coordination system which recommends dress items to customers in various ways.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.3
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• pp.349-360
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• 2019

This study analyzes the trends of recommendation services for customized fashion styles in relation to artificial intelligence. To achieve this goal, the study examined filtering technologies of collaborative, content based, and deep-learning as well as analyzed the characteristics of recommendation services in the users' purchasing process. The results of this study showed that the most universal recommendation technology is collaborative filtering. Collaborative filtering was shown to allow intuitive searching of similar fashion styles in the cognition of need stage, and appeared to be useful in comparing prices but not suitable for innovative customers who pursue early trends. Second, content based filtering was shown to utilize body shape as a key personal profile item in order to reduce the possibility of failure when selecting sizes online, which has limits to being able to wear the product beforehand. Third, fashion style recommendations applied with deep-learning intervene with all user processes of buying products online that was also confirmed to penetrate into the creative area of image tag services, virtual reality services, clothes wearing fit evaluation services, and individually customized design services.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.347-353
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• 2023

Currently, soldiers enlisted in the military (Army) are receiving measurements (automatic, manual) of body parts and trying on sample clothing at boot training centers, and then receiving clothing in the desired size. Due to the low accuracy of the measured size during the measurement process, in the military, which uses a relatively more detailed sizing system than civilian casual clothes, the supplied clothes do not fit properly, so the frequency of changing the clothes is very frequent. In addition, there is a problem in that inventory is managed inefficiently by applying the measurement system based on the old generation body shape data collected more than a decade ago without reflecting the western-changed body type change of the MZ generation. That is, military uniforms of the necessary size are insufficient, and many unnecessary-sized military uniforms are in stock. Therefore, in order to reduce the frequency of clothing replacement and improve the efficiency of stock management, deep learning-based automatic measurement of body size, big data analysis, and machine learning-based "Personalized Combat Uniform Automatic Recommendation System for Enlisted Soldiers" is proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.363-364
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• 2021

Search technology is applied to various applications using artificial intelligence technology. It is used in many ways, from identifying customer preferences to personalized recommendation systems. The purpose of this study is to develop a system for detecting adult males mainly in children's living spaces. This will prevent dangerous situations of adult intruders in advance and can be used for outsider control system. In order to develop such a system, information about clothes is used, and adult detection system is developed using various factors such as color, pattern, fashion style, and size of clothes.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.265-268
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• 2023

본 논문은 바쁘게 살아가는 사회인뿐만 아닌 합리적인 소비, 효율적인 시간 관리를 원하는 사람들을 위한 IoT 센서를 활용한 스마트 옷장을 제공하고자 한다. 기존 IoT 센서를 활용한 옷장은 의류를 청결하게 해주는 것에 제한되어 있지만, 본 논문에서 제안한 프로젝트는 옷을 청결하게 관리하는 것뿐만 아닌 본인의 옷의 정보를 DataBase에 저장하여 입력한 데이터를 기반으로 사용자의 옷에 대한 통계를 확인할 수 있으며, 이를 통해 의류 구매 서비스를 제공한다. 또한 날씨 데이터를 활용해 현재 날씨에서 오차범위를 계산하여 해당 날씨에 알맞은 옷을 추천하는 기능 및 관리 기능을 제공하여 효율적이고 편리한 의류를 관리할 수 있도록 한다.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.6
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• pp.977-987
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• 2018

This study tests the effect of recommended product presentation on consumers' usage intentions of a website, mediated by mental simulation. Mental simulation refers to perceptual experience, a more automatic form of mental imagery, initiated by exposure to the representations of objects. This study expects that when compliments of clothes (coordination items) are vertically presented online, consumers are likely to feel as if they wear the outfits due to the activation of mental simulation. The survey was conducted on 147 women in an age group between 20 and 40 years in a panel of an online survey firm. Data are analyzed using exploratory factor analysis and bootstrapping analysis by SPSS 20.0. The results show that when compliments (vs. substitutes) of clothes are presented, participants perceive a greater mental simulation. When compliments of clothes are vertically presented (vs. horizontally presented), mental simulation is also highly driven. In addition, mental simulation mediates the effects of online product presentation on consumers' usage intentions of a website. The findings of this study contribute to marketing strategies of online retailers in terms of how product recommendation can be offered to consumers with more psychological benefits.