• 한국컴퓨터정보학회논문지
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• 제15권8호
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• pp.141-148
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• 2010

모바일 단말기에서 사용자의 상황을 고려하고 사용자의 취향이나 특성을 반영하여 정보를 찾아주거나 추천하는 서비스 시스템은 개념적인 정보만을 제한적으로 추천한다. 또한 사용자의 특성에 따른 정보 선호도를 제공하지 않으므로 정확한 정보 추천의 어려운 단점이 있다. 따라서 본 논문에서는 사용자 특성에 따른 선호도를 고려하여 정확한 상황 정보를 추천 할 수 있는 개선된 k-means 알고리즘을 적용하여 사용자 특성에 따른 선호도 추천 시스템을 제안하였다. 본 연구에서는 사용자 특성에 따른 선호도를 상관 계수를 이용하여 구하고 사용자의 특성 선호도를 개선된 k-means 알고리즘을 이용하여 추천하였다. 제한적인 개념의 정보만을 제공하던 시스템에서 사용자의 특성에 따른 정보 선호도를 제공하여 정확한 정보를 추천하므로 제한된 정보 추천의 단점을 해결하였다. 성능 실험은 기존의 서비스 시스템들과 비교하여 정확도와 재현율로 대변되는 효과성을 측정하였으며, 성능 실험 결과 정확도는 85%, 재현율은 68%로 나타났다.

• 대한예방한의학회지
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• 제26권2호
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• pp.75-85
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• 2022

Objectives : The purpose of this study is to analyze the association between the satisfaction of Korean Medicine users and their intention to revisit and to recommend to others. Methods : This study conducted frequency analysis, chi-square test, and logistic regression analysis on 1,010 male and female Korean Medicine users aged 19 or higher in the country using data from the 2017 Korean Medicine Utilization and Herbal Medicine Consumption Survey. Results : The results of analyses revealed that the facilities in Korean Medicine clinics, the results of treatment, and the attitude of nurses and medical staff were the significant influencing factors on the intention to revisit and to recommend to others. Other than the above factors, the attitude toward medical treatment of Korean Medicine doctors and the satisfaction with an explanation of the treatment procedures were found to be important influencing factors on the intention to recommend to others. Conclusions : In order to increase the intention to revisit and to recommend to others a Korean Medicine clinics, the top priority lies in both proving high-quality medical services and promoting kind attitudes of medical staff.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.45-60
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• 2023

The purpose of this study is to explore factors that affect millennials and generation Z customers' perception on intention to recommend to use the digital payment services and invesetigate factors that affect perception on sustainable growth of the digital payment services. This study applied the following research questions: i) how perceived brand value, easy to use, personalization, open to public, and social value affect intention to recommend to use the digital payment services and ii) how perceived public policy, promotional strategy, and prspects affect intention to recommend to use the digital payment services to others. This study conducted an online survey. This study applied factor, ANOVA, and regression analysis to test hypotheses. The results of this study found that effects of personalization, open to public, and social value on intention to recommend the service showed significance in the case of millennials, while effects of brand value, easy to use, and open to publis on intention to recommend the service showed significance in the case of generation Z. The results provide managerial and policy implications on how to apply better strategies and pepare policies to enhance adoption of the digital payment service in cases of millennials and generation Z.

• 한국항행학회논문지
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• 제18권5호
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• pp.468-475
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• 2014

추천시스템은 선호 데이터가 대형화, 컴퓨터 처리능력과 추천 알고리즘 등에 의해 실시간 추천이 어려워지고 있다. 이에 따라 추천시스템은 대형 선호데이터를 분산처리 하는 방법에 대한 연구가 활발히 진행되고 있다. 본 논문은 하둡 분산처리 플랫폼과 머하웃 기계학습 라이브러리를 이용하여, 선호데이터를 분산 처리하는 방법을 연구하였다. 추천 알고리즘은 아이템 협업필터링과 유사한 동시발생 행렬을 이용하였다. 동시발생 행렬은 하둡 클러스터의 여러 노드에서 분산처리를 할 수 있으며, 기본적으로 많은 계산량이 필요하지만, 분산처리과정에서 계산량을 줄일 수 있다. 또한, 본 논문은 동시발생 행렬처리의 분산 처리과정을 4 단계에서 3 단계로 단순화하였다. 결과로서, 맵리듀스 잡을 감소할 수 있으며, 동일한 추천 파일을 생성할 수 있었다. 또한, 하둡 의사 분산모드를 이용하여 데이터를 처리하였을 때 빠른 처리속도를 보였으며, 맵 출력 데이터가 감소되었다.

• 유통과학연구
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• 제18권2호
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• pp.91-98
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• 2020

Purpose: This research aimed to suggest retailing companies some ways to enhance customer satisfaction with service recovery and recommendation intention towards these companies. For this purpose, current study examined the relationships among service recovery justice, service failure severity, customer trust, recovery satisfaction and intention to recommend and the moderating role of ego-resilience. Research design, data and methodology: Current study developed a structural equation model in which perceived service recovery justice is a predictor, service failure severity, customer trust, recovery satisfaction are mediators, intention to recommend is a dependent variable and the ego-resilience is a moderator between the perceived service recovery justice and the customer trust and the recovery satisfaction. Data were collected from customers who experienced service failures from retailers. A total of 400 questionnaires were collected and 365 samples were used for analysis after deleting data having missing value. SPSS 25.0 and AMOS 24.0 were used to test the validity, reliability, and structural equation modeling. Results: Empirical results showed that the perceived service recovery justice had a negative influence on the perceived service failure severity and a positive influence on the customer trust and the recovery satisfaction. These results indicate that when customers perceive the service recovery justice more highly, they perceive the service failure less severe but they perceive the retailer more trustworthy and are satisfied with service recovery. In addition, the customer trust and the recovery satisfaction had a positive influence on the intention to recommend. These results indicate that when customers perceive the retailer more trustworthy and are satisfied with service recovery, they are more intend to recommend the retailer. Moreover, the influence of the perceived service recovery justice on the customer trust and the recovery satisfaction was moderated by the ego-resilience. Conclusions: This study contributed to the service recovery literature by proving the relationship among service recovery justice, service failure severity, customer trust, recovery satisfaction and intention to recommend. Moreover, current research introduced the ego-resilience into service recovery research area and revealed the moderation role of the ego-resilience. Managerially, this research suggested retailing companies some ways to effectively recover from service failure.

• Nutrition Research and Practice
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• 제4권1호
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• pp.75-81
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• 2010

This study explored the conceptual framework of dieticians' intentions to recommend functional food and the mediating role of consumption frequency. A web-based survey was designed using a self-administered questionnaire. A sample of Korean dieticians (N=233) responded to the questionnaire that included response efficacy, risk perception, consumption frequency, and recommendation intention for functional foods. A structural equation model was constructed to analyze the data. We found that response efficacy was positively related to frequency of consumption of functional foods and to recommendation intention. Consumption frequency also positively influenced recommendation intention. Risk perception had no direct influence on recommendation intention; however, the relationship was mediated completely by consumption frequency. Dieticians' consumption frequency and response efficacy were the crucial factors in recommending functional foods. Dieticians may perceive risks arising from the use of functional foods in general, but the perceived risks do not affect ratings describing dieticians' intentions to recommend them. The results also indicated that when dieticians more frequently consume functional foods, the expression of an intention to recommend functional foods may be controlled by the salience of past behaviors rather than by attitudes.

• 한국인터넷방송통신학회논문지
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• 제21권4호
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• pp.103-108
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• 2021

주식이 오를지 내릴지를 예측하는 것은 주식의 불확실성으로 매우 어렵다. 인공지능 기술을 이용한 주가예측 방법에 대한 연구가 오랫동안 이루어져왔다. 최근에는 증권 회사에도 로봇 어드바이저라는 이름으로 인공지능 기술을 이용한 주식 매수/매도 추천 프로그램이 사용되고 있다. 본 논문에서는 인공지능 머신러닝 기술을 이용한 매수/매도 추천 시스템을 개발하기 위하여 여러 가지 기술적 분석 방법의 결과를 활용하는 이 시스템의 핵심인 엔진을 설계한다. 또한 객체지향 분석 방법을 이용한 요구사항 분석 및 플로우차트, 화면 설계 등을 보여여줌으로써 효과적인 인공지능 머신러닝 기술을 이용한 매수/매도 추천 시스템의 소프트웨어 분석 및 설계 방법을 제시하고자 한다.

• 대한예방한의학회지
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• 제27권3호
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• pp.83-95
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• 2023

Objectives : The purpose of this study is to analyze the factors affecting the intention of chronic disease patients on revisiting and recommending those clinics to others. Methods : This study conducted the frequency analysis, Rao-scott chi-square test, and logistic regression analysis on 927 people, who answered that they had been diagnosed with a chronic disease, using data from the 2020 Korean Medicine Utilization and Herbal Medicine Consumption Survey. Results : The results of analyses revealed that the attitude of medical staff, and the treatment results were the significant influencing factors on the intention to revisit and to recommend to others. Other than the above factors, the medical expenses were found to be important influencing factors on the intention to recommend to others. Conclusions : In order to increase the intention to revisit and to recommend to others a Korean Medicine clinics, the top priority lies in both proving high-quality medical services and promoting friendly attitudes of medical staff. In addition, it is necessary to actively utilize korean medicine to guarantee patients' medical options.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 컴퓨터소사이어티 추계학술대회논문집
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• pp.211-214
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• 2003

In this paper, we intend to find user's TV program choosing pattern and, recommend programs that he/she wants. So we suggest item dependency map which express relation between chosen program. Using an algorithm that we suggest, we can recommend an program, which a user has not saw yet but maybe is likely to interested in. Item dependency map is used as patterns for association in hopfield network so we can extract users global program choosing pattern only using users partial information. Hopfield network can extract global information from sub-information. Our algorithm can predict user's inclination and recommend an user necessary information.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 전기학술대회논문집
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• pp.246-250
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• 2005

Recommand Systems are being used by an ever-increasing number of E-Commerce to help customers find products to purchase. Recommend Systems offer a technology that allows personalized recommendations of items of potential interest to users based on information about similarities and dissimilarities among different customers tastes. In this paper, we design and build a Recommend System using the historical customer movie purchase transactions and extracts the knowledge needed to make association recommendations to new customers.