• 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 the Korean Home Economics Association
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• v.47 no.7
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• pp.83-95
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• 2009

This study investigated the differences in the effects of the perception of the internet fashion shopping mall characteristics on satisfaction and loyalty of internet fashion shopping mall between Korean and American college students. Questionnaires were administered to 251 Korean and 221 American college students. The results were as follows. First, service and product diversity had significant effects on satisfaction of internet fashion shopping mall in both group, while convenience had a significant impact on satisfaction of internet fashion shopping mall in American group. Second, service, product diversity, and promotion had significant influences on repurchase intention of internet fashion shopping mall in Korean consumer group, while service and reliability, product diversity, and convenience had significant effects on repurchase intention of internet fashion shopping mall in American consumer group. Third, service had a significant effect on recommendation intention in Korean group, while service and reliability and product diversity had significant influences on recommendation intention in American group.

• Korea Real Estate Review
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• v.24 no.1
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• pp.91-104
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• 2014

This paper studied condo selection attributes that affected satisfaction, recommendation and revisitation, in particular, investigated gender and age differences. Research target is the group who revisited time-sharing condominium within one year. The paper seeks to understand factors that affect and contribute to customer satisfaction and intentions for reuse. This study model was analyzed by the basic statistical analysis, factor analysis, reliability analysis and multiple analysis, using SPSS 18.0 and AMOS 18.0. We found that 5 condo selection attributes that have significant affect on user satisfaction: facility, service, product, accessibility and expense. Furthermore it was evident that user satisfaction has a significant effect on condo recommendation and intentions of reuse. With regard to sex, for male users expense, accessibility and service had a significant effect on their satisfaction level, while for female users, product was most important. User satisfaction both have a significant effect on recommendation and intentions of reuse but for females this was more evident. Regarding the age, for 20~30 age band, service and product factor had a significant effect on user satisfaction in order, whereas, for the age band of over 40s, expense, product and facility factors were important. User satisfaction of both have a significant effect on recommendation and intentions of reuse. In the meantime user satisfaction of 20~30 age band had a bigger positive significant effect on recommendation and intentions of reuse than the age band over 40s.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.19-24
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• 2020

Recently, due to the rapid increase of pets, there is a need for an integrated pet-related personalized product recommendation service such as feed recommendation using a health status check of pets and various collected data. This paper implements a product recommendation system that can perform various personalized services such as collection, pre-processing, analysis, and management of pet-related data using big data. First, the sensor information worn by pets, customer purchase patterns, and SNS information are collected and stored in a database, and a platform capable of customized personalized recommendation services such as feed production and pet health management is implemented using statistical analysis. The platform can provide information to customers by outputting similarity product information about the product to be analyzed and information, and finally outputting the result of recommendation analysis.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.8
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• pp.890-905
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• 2011

This study investigated service quality perception, purchase satisfaction, recommendation intention, and switching intention of fashion consumers according to the types of internet shopping malls. The survey was conducted from February 7 to 21 in 2011, and 294 responses were used in the data analysis. The statistical analysis methods were frequency analysis, factor analysis, reliability analysis, ANOVA, and regression analysis. The result of the service quality perception of internet fashion consumers was classified by site characteristics, reliability, enjoyment, product diversity, responsibility, security, and order convenience. There were significant differences in the site characteristics, reliability, enjoyment, responsibility, and security of service quality perception by the types of internet shopping malls. In addition, the factors of service quality perception that could affect the purchase satisfaction of fashion consumers showed differently according to the types of internet shopping malls. The purchase satisfaction of fashion consumers influenced the recommendation intention in all types of internet shopping malls and the purchase satisfaction influenced the switching intention in fashion specialized internet shopping malls.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.501-506
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• 2022

The prolonged of the Post Corona, many Cosmetic company launched various online services. In this paper, consider about the quality of product recommendation using personal color detecting technology. Using the detecting tool which is most widely used by cosmetic company. we will do a lot of testing with this tool and also testing with color detecting equipment. For precise experimental results, it was conducted in a consistent experimental environment. This experiment can be a foundation that can be well used for the expansion of personalized product recommendation services according to the current image-based skin color measurement.

• The Journal of Bigdata
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• v.7 no.2
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• pp.173-193
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• 2022

With the revision of the Data 3 Act, the financial MyData industry was created newly. MyData services collect the financial customers' data scattered in various financial companies and provide personalized services such as personal financial product recommendation, personal expenditure advice, etc. Although MyData service started in 2022, but the use of the service has not been significantly activated. This study attempted to analyze the factors affecting the use of MyData services from the perspective of financial consumers through VAM, UTAUT2 model. The factors related to the perceived value and intention to use MyData services of financial consumers were verified using benefit and sacrifice variables. Personal Innovativeness was used as a moderating variable. As a result of this study, it was found that personal product recommendation service has an important influence on the use of MyData services, and personal innovativeness has an effect as a modulating variable. It can be said that it is meaningful as a preceding study in terms of timing because it studied the perceived value of consumers less than a year after the MyData service began. From the practical perspectives, it was possible to show the change direction and marketing points of the MyData service. In practice, it was possible to confirm the direction of the service and the marketing point.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.211-219
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• 2022

The purpose of this study is to improve the operation of shopping mall by examining the effect of service characteristics of Greenanum on customer satisfaction, repurchase and recommendation intention. The hypothesis was verified with SPSS22.0 and PROCESS macro 3.5. As a result, some hypotheses were supported between satisfaction, repurchase, and recommendation for service characteristics. Second, positive effects were found between satisfaction and repurchase, and recommendation intention. Third, a mediating effect appeared. Implications include improvement of low site awareness, benchmarking, and product quality improvement. In the future, it will be necessary to study the differences in the various characteristics of the products sold rather than the differentiation of the shopping mall itself.

• The Research Journal of the Costume Culture
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• v.17 no.3
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• pp.367-382
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• 2009

The aim of the current study was to investigate the impact of store attribute satisfaction on intentions of revisit and recommendation among clothing consumers. The data were collected from 319 consumers through survey and frequency analysis, reliability analysis, factor analysis, and multiple regression analysis were used to obtain results. The findings were as follows: 1. From factor analysis, seven factors were distracted: Fact 1(brand and price), Fact 2(store's facility and environment), Fact 3(product), Fact 4(transportation convenience and access), Fact 5(selling and advertisement), Fact 6(store's atmosphere), and Fact 7(salesman's service). 2. Four factors had statistically significant influence on overall satisfaction of clothing consumers. The most influential factor was Fact 2(store's facility and environment) and Fact 5(selling and advertisement), Fact 1(brand and price), and Fact 4(transportation convenience and access) showed their effects on overall satisfaction in an hierarchical rank-order following Fact 2. 3. Four factors such as Fact 2(store's facility and environment), Fact 1(brand and price), Fact 4(transportation convenience and access) and Fact 5(selling and advertisement) in an hierarchical rank-order from Fact 1 had statistically significant impact on intentions of revisit. 4. Six factors such as Fact 1(brand and price), Fact 2(store's facility and environment), Fact 3(product), Fact 5(selling and advertisement), Fact 6(store's atmosphere), and Fact 7(salesman's service) in an hierarchical rank-order from Fact 1 had statistically significant influence on the intention of recommendation. 5. The results further showed that among seven factors, Fact 1(brand and price), 'Fact 2(store's facility and environment), and Fact 5(selling and advertisement) had impact on both the intention of revisit and the intention of recommendation.