• 경영과학
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• 제17권3호
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• pp.19-30
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• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.93-97
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• 2002

In this paper, we propose personalized recommendation techniques based on multidimensional scaling (MDS) method for Business to Consumer Electronic Commerce. The multidimensional scaling method is traditionally used in marketing domain for analyzing customers' perceptional differences about brands and products. In this study, using purchase history data, customers in learning dataset are assigned to specific product categories, and after then using MDS a positioning map is generated to map product categories and alternative advertisements. The positioning map will be used to select personalized advertisement in real time situation. In this paper, we suggest the detail design of personalized recommendation method using MDS and compare with other approaches (random approach, collaborative filtering, and TOP3 approach)

• 한국시뮬레이션학회논문지
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• 제14권3호
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• pp.137-146
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• 2005

In parametric approach to a signature verification, it generally uses so many redundant features unsuitable for each individual signature that it causes harm, instead. This paper proposes a method of determining personalized weights of a feature set in signature verification with parametric approach by identifying the characteristics of each feature. For an individual signature, we define a degree of how difficult it is for any other person to forge the one's (called 'DFD' as the Degree of Forgery Difficulty). According to the statistical characteristics and the intuitional characteristics of each feature, the standard features are classified into four types. Four types of DFD functions are defined and applied into the distance calculation as a personalized weight factor. Using this method, the error rate of signature verification is reduced and the variation of the performance is less sensitive to the changes of decision threshold.

• 대한한의학회지
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• 제35권3호
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• pp.40-48
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• 2014

Objectives: This study aimed to review the characteristics of personalized medicine and Korean medicine, and the correlation between personalized medicine and Korean medicine. Methods: We investigated various studies in PubMed, Scopus and domestic Korean medicine journals. In addition, we discussed the topic based on literature. Results: Western medicine developed as evidence-based medicine. However, its limitations are being reached, so a new paradigm of medicine is needed. As a result, personalized medicine has appeared. Recently, through the development of human genomics, personalized medicine has been researched on the basis of individual genetic characteristics. Korean medicine has developed with a unique holistic approach and treats not the disease itself but the patient's body. Its characteristic is well expressed through syndrome differentiation and treatment. Syndrome differentiation represents the nature of person-centered medicine and becomes the root of personalized medicine. Conclusions: Compared with genome-based personalized medicine of Western medicine, Korean medicine could be classified as syndrome-based personalized medicine. It would be great to apply this characteristic to clinical practices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.446-465
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• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• 한국융합학회논문지
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• 제11권3호
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• pp.67-75
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• 2020

난이도는 학습자가 컨텐츠를 선택하는 중요한 기준 중 하나이다. 하지만, 대부분의 난이도 기준은 컨텐츠 제공자가 획일적으로 결정한다. 이러한 방식으로는 학습자의 다양한 수준과 환경을 고려한 맞춤형 교육을 지원할 수 없다. 본 연구는 이 문제를 해결하기 위하여 학습자와 컨텐츠의 지식을 정형화하고 일반화한 후, 이를 실험하기 위한 객체 모델과 맞춤형 난이도 척도를 설계하였다. 또한, 이를 검증하기 위한 목적으로 구현한 도구를 이용하여 100개의 음악 교육 컨텐츠와 20명의 학습자를 기반으로 시뮬레이션을 진행했다. 실험 결과는 제안한 방법이 학습 상태와 컨텐츠에서 정의한 지식의 유사도를 이용하여 맞춤형 난이도를 계산할 수 있음을 보여 주었다. 제안한 접근법은 학습 상태와 컨텐츠에 쉽게 접근할 수 있는 온라인 학습 시스템에 효과적으로 적용할 수 있다.

• 경영과학
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• 제21권3호
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• pp.23-38
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• 2004

Recently the usage of mobile devices which enable the accessibility to Internet has been dramatically increased. Most of the mobile services, however, so far tend to be simple such as infotainment service. In order to fully taking advantage of wireless network and corresponding technology, personalized web service based on user's context could be needed. Meanwhile, optimization techniques have been vitally incorporated for optimizing the development and administration of electronic commerce. However, applying context-aware optimization mechanism to personalized mobile services is still very few. Hence, the purpose of this paper is to propose a methodology to incorporate optimization techniques into personalization services. Multi agent-based web service approach is considered to realize the methodology. To show the feasibility of the methodology proposed in this paper, a prototype system, CAMA-myOPt(Context-Aware Multi-Agent system for my Optimization), was implemented and adopted in mobile comparative shopping.

• Asian Pacific Journal of Cancer Prevention
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• 제14권3호
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• pp.1661-1664
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• 2013

Recently there have been numerous advances in understanding the genetic basis of cancer which have resulted in more appropriate treatments. In this paper we describe the experience of the Burzynski Clinic, involved in treatment of numerous patients based on personalized approach using novel combinations for difficult-to-treat malignancies, with gynecological cancers. This retrospective study was conducted by extracting data from Burzynski Clinic's medical records and comprehensive review. Among the advanced refractory ovarian cancers cases (N=33), an objective response (OR) was found in 42.4%. We anticipate that with improved technology and novel therapeutics this rate will increase and adverse events will be reduced.

• Journal of Microbiology and Biotechnology
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• 제32권12호
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• pp.1497-1505
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• 2022

Recently, the concept of personalized nutrition has been developed, which states that food components do not always lead to the same metabolic responses, but vary from person to person. Although this concept has been studied based on individual genetic backgrounds, researchers have recently explored its potential role in the gut microbiome. The gut microbiota physiologically communicates with humans by forming a bidirectional relationship with the micronutrients, macronutrients, and phytochemicals consumed by the host. Furthermore, the gut microbiota can vary from person to person and can be easily shifted by diet. Therefore, several recent studies have reported the application of personalized nutrition to intestinal microflora. This review provides an overview of the interaction of diet with the gut microbiome and the latest evidence in understanding the inter-individual differences in dietary responsiveness according to individual baseline gut microbiota and microbiome-associated dietary intervention in diseases. The diversity of the gut microbiota and the presence of specific microorganisms can be attributed to physiological differences following dietary intervention. The difference in individual responsiveness based on the gut microbiota has the potential to become an important research approach for personalized nutrition and health management, although further well-designed large-scale studies are warranted.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1141-1155
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• 2019

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities' detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.