• Fashion & Textile Research Journal
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• v.23 no.2
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• pp.200-211
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• 2021

This study examines the effects of fashion influencers' body types (realistic versus ideal body types) on self-expression, self-representation, and recommendation intentions, as mediated by familiarity toward influencers. Although fashion influencers lead to a positive consumer response compared to traditional advertisements, previous research on the effects of fashion influencers on consumers is limited. Thus, this study tests the role of consumers' socio-psychological aspects in understanding how and why fashion influencers affect consumers' behavioral intentions associated with self-expression, self-representation, and influencer recommendation. A total of 180 women in their 20s and 30s participated in the survey. The responses were collected after showing them stimuli featuring fashion influencers with either ideal or realistic body shapes. The data were analyzed using SPSS18.0 for descriptive statistics, and AMOS 18.0 for confirmatory factor analysis and structural equation modeling. The results showed that participants who were shown realistic body types perceived familiarity, which generated positive effects on self-expression, self-representation, and recommendation intentions. Hence, the effects of influencers' body types on recommendation intention are mediated by familiarity. Self-expression and self-representation intentions also increase influencer recommendation intention. Comparatively, participants who were shown ideal body types only induced higher self-representation intention, which increased their recommendation intention. The current findings can help fashion marketers select the appropriate influencers who fit their target customers as promotional models, as well as to induce changes in consumers' behavioral intention.

• Journal of the Korea Fashion and Costume Design Association
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• v.24 no.2
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• pp.59-72
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• 2022

The online fashion market in the 21st century has shown rapid growth. Against this backdrop, using consumer activity data to provide customized customer services has emerged as a viable business model that draws attention. Algorithm-based personalized recommendation services are a good example. But their application in fashion products has clear limitations. It is not easy to identify consumers' perceptions of the attributes of fashion, which are various, hard to define, and very sensitive to trends. So there is a need to compile data on consumers' underlying awareness and to carry out defined research to increase the utilization of such services in the fashion industry and further engage consumers. This research aims to classify the attributes and types of fashion products and to identify consumers' perceptions of a given situation where a recommendation service is offered. To find out consumers' perceptions of and satisfaction with recommendation services, an online and mobile survey was conducted on women in their 20s and 30s, a group that uses recommendation services frequently. A total of 455 responses were used for analysis. SPSS 28.0 was used, combined with Conjoint Analysis and multiple regression, to analyze data. The study results could provide insights into a better understanding of recommendation services and be used as basic data for companies to identify consumers' preferences and draw up a detailed strategy for market segmentation.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.242-249
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• 2018

In this paper, the customized sports convergence contents curation system is proposed for activation of life sports. The proposed system collects and analyzes profile of social sports group (club, society, etc.) for recommending optimized sports convergence contents to user. In addition, the feedback based on the recommendation result from the user is continuously reflected and the optimal recommendation is made possible. For the system evaluation, the proposed system is tested to 300 users (about 20 sports team) for about 3 months and the system is verified by analyzing the initial recommendation results and recommendation results reflected by user feedback.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.279-284
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• 2017

The visual impact of fonts on lots of documents and design work is significant. Accordingly, the users desire to appropriately use fonts suitable for their intention. However, existing font recommendation programs are difficult to consider what users want. Therefore, we propose a font recommendation system based on user-evaluated font attribute value. The properties of a font are called attributes. In this paper, we propose a font recommendation module that recommends a user 's desired font using the attributes of the font. In addition, we classify each attribute into three types of usage, personality, and shape, suggesting the font that is closest to the desired font, and suggest an optimal font recommendation algorithm. In addition, weights can be set for each use, personality, and shape category to increase the weight of each category, and when a weight is used, a more suitable font can be recommended to the user.

• Journal of the Korea Fashion and Costume Design Association
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• v.23 no.2
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• pp.53-65
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• 2021

The study of behavioral intention in the fashion industry, until recently, focused on the variables of satisfaction and the intention of purchase, and were limited to specific individuals who were fashion consumers. It was found that variables of intention of recommendation have a greater impact on many other potential customers from a study on consumer behavioral intention. Thus, this study seeks to examine the relationship between the selection factors of collaboration fashion products and preference, and the relationship between preference and the intention of purchase. Moreover, the purpose of this research is to find the influence of relationships between selective factors of collaboration fashion products and the intention of recommendation. The 'intention of recommendation' was set as a dependent variable, that influences the relationship between preference and intention of recommendation. For empirical analysis, SPSS 25.0 software was used to conduct frequency analysis, reliability analysis, factor analysis, and multiple regression analysis based on a survey results conducted upon 217 people in their 20s. The empirical analysis results are as follows: First, collaboration fashion product selection factors consisted of 'product originality', 'designers and artists' reputation', 'product reliability', and 'products' aesthetic impression'. Second, the selection factors of fashion products had a positive influence on product preferences. Third, the preference for collaboration fashion products had an influence on intention of purchase and intention of recommendation. Fourth, collaboration fashion product selection factors affected intention of purchase. Fifth, selection factors of collaboration fashion products were found to have a significant impact on the intention recommendation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3266-3285
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• 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.

• 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.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.73-82
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• 2014

SNS becomes a recent issue, and many researches in various kinds of field are being done by taking advantage of it. Especially, there are many researches existed on the system that finds user's interest and makes recommendation based on multiple social data generated on the SNS. User's interest is not only revealed from the user's writing but also from the user's relationship with friends. This study proposes a recommendation method that extracts user's interest by using social relationship and its categorization applies it to the recommendation. In this way, it can recommend user's interest with category based on the writings by the user and furthermore it can apply the user's relationship with his/her friends for more accurate recommendation. In addition, if necessary, the recommendation can be made by extracting any interest shared between the user and specific friends. Through experiments, we show that our method using social category can produce satisfactory result.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.37-45
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• 2022

As of 2020, about 500 hours of videos are uploaded to YouTube, a representative online video platform, per minute. As the number of users acquiring information through various uploaded videos is increasing, online video platforms are making efforts to provide better recommendation services. The currently used recommendation service recommends videos to users based on the user's viewing history, which is not a good way to recommend videos that deal with specific purposes and interests, such as educational videos. The recent recommendation system utilizes not only the user's viewing history but also the content features of the item. In this paper, we extract the content features of educational video for educational video recommendation based on YouTube, design a recommendation system using it, and implement it as a web application. By examining the satisfaction of users, recommendataion performance and convenience performance are shown as 85.36% and 87.80%.

• Journal of Korea Trade
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• v.27 no.1
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• pp.42-59
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• 2023

Purpose - As a leading source of foreign exchange and investment, tourism has grown in importance as a component of international trade. Accordingly, in recent decades much attention has been directed toward attracting foreign tourists and, in turn, positively affecting the recommendation intentions of foreign tourists. Despite such interests, there remains a dearth of empirical research on this issue. Moreover, prior research has focused primarily on the simple main effect of a certain factor on recommendation intentions. Therefore, the present study aims to (1) investigate the effect of overall satisfaction on the recommendation intentions of foreign tourists, and (2) examine the potential moderating effects of personal factors (i.e., age and destination image) on the association between overall satisfaction and recommendation intention. Design/methodology - Using a moderated moderation analysis of the data drawn from the 2018 International Visitor Survey conducted by the Korea Tourism Organization, this study proposes the three-way interaction effects of overall satisfaction, age, and destination image on recommendation intention. Findings - The findings of the study indicate that overall satisfaction is positively associated with recommendation intention and this relationship becomes stronger among younger tourists. The findings further indicate that the moderating effect of age on the relationship between overall satisfaction and recommendation intention depends on changes in the image of the destination. Specifically, the destination image exerts a positive moderating impact on the influence of age that moderates the overall satisfaction and recommendation intention relationship. Originality/value - Considering that the tourism economy has been severely affected by the current COVID-19 pandemic, this study contributes to a more accurate understanding of the factors affecting the recommendation intention, especially in times of crisis.