• Journal of Korea Society of Industrial Information Systems
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• v.9 no.3
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• pp.55-63
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• 2004

Most of current shooing malls on the internet do not satisfy all customers because they present arrangements of goods and suggestions uniformly and comprehensively according to the thinking of their managers. When classifying into groups according to generations, gender, income, job, hobby, etc. the propensity of purchase is showed differently and the interest and real purchasing power of the customer is different in shopping malls. This paper describes the development of customer oriented intelligent shopping mall system that is added not only statistical analysis dynamical activity of customers but also weight and construct optimal according to group of goods automatically.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7459-7466
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• 2014

This study examined the purchasing behavior of cosmetics among Japanese, Chinese tourists and Koreans, as well as the differences according to nationality. Based on a literature study, a questionnaire was developed and a field survey was performed. A self-administered survey was conducted throughout Myungdong and Suwon. The 251 usable questionnaires were collected. A Reliability test, Factor analysis, Regression test, and ANOVA were used. The purchasing behavior of cosmetics has 5 factors gained by the factor analysis, including the functionality of cosmetics, the newest one of the cosmetics, brand-oriented cosmetics, impulsive purchase, and physical evidence. In addition, the purchasing behavior of cosmetics is becoming complicated, and the attitudes to cosmetics has changed. Finally, there are a few differences among Japanese, Chinese tourists and Koreans. According to this research, cosmetic companies must underline their brand and focus on managing the cosmetic functionality, physical evidence of the store, and the skill and attitudes of point of sales.

• Bulletin of Food Technology
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• v.12 no.2
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• pp.10-25
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• 1999

전체적인 국민 생활수준의 향상에 따라 건강에 대한 관심이 매우 높아지면서 고품질의 신선 식품에 대한 수요가 급격히 늘고 있는 추세이다. 그 가운데에서도 과일, 채소류의 비중이 점차 증대되고 있으며, 이들의 가공제품보다는 신선한 식품에 대한 소비 성향이 괄목할 만한 성장세를 나타내고 있다. 이와 함께 신선 과일, 채소류의 소비에 있어 변화하고 있는 또 다른 경향은 소비자가 이용시 간편성과 합리성을 추구하고 있다는 점이다. 이에 따라 최근에는 각종 신선편의식품이 시대의 조류에 맞춰 출시되고 있으며, 특히 이들 상품에 대한 수요는 여성의 사회진출, 맞벌이 부부의 증가, 독신자의 증가, 노인층 구매력의 증가 등 여러 가지 사회환경 변화에 부응하여 더 더욱 증대할 것으로 판단된다. 이에 본고에서는 한국식품개발연구원이 보건복지부의 연구비 지원으로 수행중인 보건의료기술 개발과제와 관련하여 신선 과채류 편의식품의 안전성 및 저장성 측면에서 품질향상을 위해 적용할 수 있는 각종 새로운 기술, 즉 원재료에서 포장된 상품 상태까지 과일, 채소류의 최소가공 과정을 포함한 전 단계의 처리방법을 소개하고 자 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.185-190
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• 2006

Most systems in ubiquitous computing analyze context information of users which have similar propensity with demographics methods and collaborative filtering to provide personalized recommendation services. The systems have mostly used static context information such as sex, age, job, and purchase history. However the systems have limitation to analyze users' propensity accurately and to provide personalized recommendation services in real-time, because they have difficulty in considering users situation as moving path. In this paper we use users' moving path of dynamic context to consider users situation. For the prediction accuracy we complete with a path completion algorithm to moving path which is inputted to RSOM. We train the moving path to be completed by RSOM, analyze users' moving pattern and predict a future moving path. Then we recommend the nearest product on the prediction path with users' high preference in real-time. As the experimental result, MAE is lower than 0.5 averagely and we confirmed our method can predict users moving path correctly.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.6
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• pp.107-119
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• 2015

Recently, middle and low social class entered in the upper class for the specific reasons and joined reference group in order to present demonstration effect. This phenomenon was assumed the consumer's desire for housing estate not from the leisure class. The superiority which feels belong to reference group, that was able to purchase high price product and the competence which can afford a high price goods are effective in housing estate. The study had been researched for Veblen effect according to residence estate. The partial least squares path modeling was used for Veblen effect affected by various psychological attributes, housing patterns and demographics for analysis. As a result, the conspicuous consumption from tenants was higher than residents, the social face sensitivity affected positively for self esteem and housing pattern, therefore, it caused conspicuous consumption. Moreover, we found conspicuous consumption had been increased followed by more spacious housing and higher income.

• Asia Marketing Journal
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• v.13 no.3
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• pp.209-231
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• 2011

Internet is having strong impact on the consumer's decision making process. Information search has been done actively through internet today. The online reviews can be crucial information cue to evaluate the alternarive products. The online WOM(Word-Of-Mouth) effect depends on the characteristics of information sender, receiver, and WOM. This study is to examine the influence of the online word of mouth on the consumer purchase intention and the moderating role of product involvement, consumer regulatory focus and self-efficacy. Positive customer reviews on the products influence the purchase intention positively and negative customer reviews influence it negatively. Moderating role of involvement in the causal relation between the valence of online reviews and purchase intention is tested. In case of positive WOM, it is predicted that purchase intention for high involvement products is higher than that of low involvement. In case of negative WOM, purchase intention for high involvement product is lower than that of low involvement product. And this study invetigate the moderating role of regulatory focus. In case of positive WOM, it is predicted that promotion focus oriented consumers have higher purchase intention than prevention focus oriented consumers. In case of negative WOM, prediction is that prevention focus oriented consumers have lower purchase intention than promotion focus oriented consumers. Then we examine the moderating role of self efficacy in the causal relation between the valence of online reviews and purchase intention. In case of positive WOM, it is predicted that consumers with low self efficacy have higher purchase intention than consumers with high self efficacy. In case of negative WOM, it is predicted that consumers with low self efficacy have lower purchase intention than consumers with high self efficacy. Emprical results support our prediction and four hypotheses derived from our conceptual framework are all accepted. This study suggest that the level of product involvement, consumer regulatory focus and the level of self-efficacy influence the consumer responses of the valence of online reviews. Therefore marketers need to manage online reviews based on the level of product involvement, regulatory focus orientation and the level of self-efficacy of target consumers.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.191-205
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• 2005

This paper presents a novel product recommender system as a tool fur differentiated marketing service of online shopping malls. Ihe proposed model uses genetic algorithnt one of popular global optimization techniques, to construct a personalized product recommender systen The genetic algorinun may be useful to recommendation engine in product recommender system because it produces optimal or near-optimal recommendation rules using the customer profile and transaction data. In this study, we develop a prototype of WeLbased personalized product recommender system using the recommendation rules fi:om the genetic algorithnL In addition, this study evaluates usefulness of the proposed model through the test fur user satisfaction in real world.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.6
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• pp.253-260
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• 2015

When we buy items in online stores, it is common to face recommended items that meet our interest. These recommendation system help users not only to find out related items, but also find new things that may interest users. Recommendation system has been widely studied and various models has been suggested such as, collaborative filtering and content-based filtering. Though collaborative filtering shows good performance for predicting users preference, there are some conditions where collaborative filtering cannot be applied. Sparsity in user data causes problems in comparing users. Systems which are newly starting or companies having small number of users are also hard to apply collaborative filtering. Content-based filtering should be used to support this conditions, but content-based filtering has some drawbacks and weakness which are tendency of recommending similar items, and keeping history of a user makes recommendation simple and not able to follow up users preference changes. To overcome this drawbacks and limitations, we suggest weighted window assisted user history based recommendation system, which captures user's purchase patterns and applies them to window weight adjustment. The system is capable of following current preference of a user, removing useless recommendation and suggesting items which cannot be simply found by users. To examine the performance under user and data sparsity environment, we applied data from start-up trading company. Through the experiments, we evaluate the operation of the proposed recommendation system.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.147-168
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• 2011

As personalized recommendation of products and services is rapidly growing in importance, a number of studies provided fundamental knowledge and techniques for developing recommendation systems. Among them, the CF technique has been most widely used and has proven to be useful in many practices. However, current collaborative filtering (CF) technique has still considerable rooms for improving the effectiveness of recommendation systems: 1) a similarity function most systems use to find so-called like-minded people is not well defined in that similarity is computed from a single perspective of similarity concept; and 2) temporal information that contains the changing preference of customers needs to be taken into account when making recommendations. We hypothesize that integration of multiple aspects of similarity and utilization of temporal information will improve the accuracy of recommendations. The objective of this paper is to test the hypothesis through a series of experiments using MovieLens data. The experimental results show that the proposed recommendation system highly outperforms the conventional CF-based systems, confirming our hypothesis.