• Journal of Digital Contents Society
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• v.15 no.6
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• pp.711-719
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• 2014

Along with the advent of ubiquitous computing environment, it is becoming a part of our common life style that the demands for enjoying the wireless internet using intelligent portable device such as smart phone and iPad, are increasing anytime or anyplace without any restriction of time and place. The recommending service becomes a very important technology which can find exact information to present users, then is easy for customers to reduce their searching effort to find out the items with high purchasability in e-commerce. Traditional mining association rule ignores the difference among the transactions. In order to do that, it is considered the importance of type of merchandise or service and then, we suggest a new recommending service using mining sequential pattern based on weight to reflect frequently changing trends of purchase pattern as time goes by and as often as customers need different merchandises on e-commerce being extremely diverse. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

• 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.

• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.8
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• pp.1139-1154
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• 2013

This study examined the effects of consumers' usefulness and the hedonic perception of their willingness to provide information and cooperation intention in the use of location-context based mobile product recommendation services for fashion stores. We examined the influence of consumers' beliefs regarding marketer's information practices on their perceptions of provided services. In addition, the moderating effects of consumers' epistemic curiosity and information control level were investigated. A total of 400 smartphone users were included as participants for the present study. The results showed that consumers who perceived information services as more hedonic and useful are more likely to provide personal information and cooperate with marketers. The findings of the study suggest that fashion retailers who plan to introduce mobile product recommendation services should pay attention to the hedonic aspects of the services. In addition, the effects of usefulness and hedonic perception of the two dependent variables were different according to the level of epistemic curiosity and information control.

• Information Systems Review
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• v.22 no.4
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• pp.75-92
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• 2020

As population and generation structures change, more and more customers tend to avoid facing relation due to the development of information technology and spread of smart phones. This phenomenon consists with efficiency and immediacy, which are the consumption patterns of modern customers who are used to information technology, so offline network-oriented distribution companies actively try to switch their sales and services to untact patterns. Recently, untact services are boosted in various fields, but beauty products are not easy to be recommended through untact services due to many options depending on skin types and conditions. There have been many studies on recommendations and development of recommendation systems in the online beauty field, but most of them are the ones that develop recommendation algorithm using survey or social data. In other words, there were not enough studies that classify segments based on user information such as skin types and product preference. Therefore, this study classifies customer segments using machine learning technique K-prototypesalgorithm based on customer information and search log data of mobile application, which is one of untact services in the beauty field, based on which, untact marketing strategy is suggested. This study expands the scope of the previous literature by classifying customer segments using the machine learning technique. This study is practically meaningful in that it classifies customer segments by reflecting new consumption trend of untact service, and based on this, it suggests a specific plan that can be used in untact services of the beauty field.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.293-301
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• 2017

Most new products not only suddenly disappear in the market but also quickly cannibalize older products. Under such a circumstance, retailers may have too much stock, and customers may be faced with difficulties discovering products suitable to their preferences among short life cycle products. To address these problems, recommender systems are good solutions. However, most previous recommender systems had difficulty in reflecting changes in customer preferences because the systems employ static customer preferences. In this paper, we propose a recommendation methodology that considers dynamic customer preferences. The proposed methodology consists of dynamic customer profile creation, neighborhood formation, and recommendation list generation. For the experiments, we employ a mobile image transaction dataset that has a short product life cycle. Our experimental results demonstrate that the proposed methodology has a higher quality of recommendation than a typical collaborative filtering-based system. From these results, we conclude that the proposed methodology is effective under conditions where most new products have short life cycles. The proposed methodology need to be verified in the physical environment at a future time.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.86-93
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• 2012

The emergence of cable TV, satellite broadcasting and IPTV provides viewers with a variety of TV programs. However, viewers' desire for watching their favorite TV program at convenient time has increased because of insufficient spare time. As an increase in smart phone market has accelerated an entry into "the age of smart network media" since 2009, mobile media suggests services connected to other digital devices. Recently, there has been growing interest in TV controling system of smart phone. Therefore, the present study aims to provide an concept of the smart phone application which recommends contents of TV program by analyzing personal watching pattern. To suggest detailed direction of the interaction and UI design, we analyzed previous research and examples of TV controlling applications and products. In addition, public opinion survey was carried out to rationalize this study and suggest suitable UI structure.

• Knowledge Management Research
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• v.23 no.2
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• pp.193-210
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• 2022

The rapid development of mobile technology and the improvement of network speed are providing convenience to various services, and mobile shopping malls are no exception. Although efforts are being made to promote sales by combining various technologies such as customized recommendations using big data and specialized personalization services based on artificial intelligence, most mobile shopping malls have the same detailed page information structure including detailed product information. In this context, in this study, it was determined that the content layout of the product detail page and the mobile product detail page layout tailored to the consumer's preference should be presented according to the consumer's preference. Based on Higgins' Regulatory Focus Theory, a study of consumer propensity revealed that the content layout arrangement on a product detail page, when presented in an F-shape, informs the consumer that it is organized. If presented in a Z-shape, vivid information was recognized, and it was examined whether the product attitude and purchase intention were affected. As a result, when the content layout composition was presented as a layout arrangement in the form of a sense of unity and organization, prevention-focused consumers were positively affected by product attitudes and purchase intentions, and promotion-oriented consumers felt freedom. When presented in an arrangement, it was confirmed that the product attitude and purchase intention were affected.

• Journal of Fashion Business
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• v.25 no.5
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• pp.25-37
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• 2021

As the online shopping market grows, channels in the mobile shopping environment have become increasingly diverse as a wide variety of products are introduced every day. This study investigated the effects of the self-construal type, mobile product recommendation system type, and fashion product type on purchase intention. The experimental design of this study was a 2 (self-construal type: independent vs. interdependent) × 2 (product recommendation system: bestseller vs. content-based) × 2 (fashion product type: utilitarian vs. hedonic) 3-way mixed ANOVA. Women (n = 387) in their 20 to 30s residing in Seoul and the Gyeonggi area participated in the study. The data were analyzed with the SPSS 24 program and 3-way ANOVA and simple main effects analyses were conducted. The results were as follows. First, self-construal, product recommendation, and fashion product types had a statistically significant impact on purchase intention. Second, fashion product and consumers' self-construal types had significant interaction effects on purchase intention. Finally, product recommendation and fashion product and self-construal types showed significant 3-way interaction effects on purchase intention. The study confirmed an interaction between the self-construal, type of product recommendation system, and the type of fashion product used in influencing purchase intention.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1042-1045
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• 2013

정보기술의 발달로 전 세계에서 발생하는 사건 사고들은 실시간으로 확인 가능하며 정보의 중요성은 더욱 더 중요해지고 있다. 이런 사회 현상에 맞춰 인적자원 솔루션에서도 빅 데이터 분석 기술을 이용하여 인적자원 의사결정에 도움을 주는 기술이 필요하게 되었다. 따라서 본 논문에서는 빅 데이터 분석 기술을 이용하여 인사채용과 관련된 데이터들을 추출하고 분석하여 구직자의 적성과 능력에 맞는 직업을 예측하는 시스템을 설계하였다. 구직자 및 이직을 원하고 있는 사람들이 소셜 네트워크 서비스를 이용하면서 사용하고 있는 특정 단어와 특정 단어의 언급 빈도의 데이터를 추출하고 추출 된 데이터는 통계를 내어 데이터의 특성에 맞게 분류하여 분류된 데이터는 연관된 속성에 의해 그룹화 한다. 그룹화 된 정보를 분석하여 구직자의 적성과 능력을 고려한 직업을 예측하는 정보로 도출하여 직업을 추천 할 수 있는 예측 시스템을 설계하였다.