• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.343-350
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• 2020

In the era of the Fourth Industrial Revolution in the digital revolution is taking place, various attempts have been made to provide various contents in a digital environment. In this paper, agent-recruitment service system based on collaborative deep learning is proposed for the intelligent head hunting service. The service system is improved from previous research [7] using collaborative deep learning for more reliable recommendation results. The Collaborative deep learning is a hybrid recommendation algorithm using "Recurrent Neural Network(RNN)" specialized for exponential calculation, "collaborative filtering" which is traditional recommendation filtering methods, and "KNN-Clustering" for similar user analysis. The proposed service system can expect more reliable recommendation results than previous research and showed high satisfaction in user survey for verification.

• 한국식생활문화학회지
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• 제29권6호
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• pp.549-556
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• 2014

This study aimed to determine consumer satisfaction according to selection attributes of specialized coffee shops and also understand the effects of consumer satisfaction on recommendation intention and revisit intention. Through positive analysis, the study produced the following results. In the factor analysis of selection attributes of specialized coffee shops, there were six factors: 'quality', 'brand image', 'economic feasibility', 'menu diversity', 'the atmosphere and convenience of the shop', and 'service'. Among these factors, 'brand image', 'economic feasibility', and 'menu diversity' were found to exert a significant influence on consumer satisfaction. Second, consumer satisfaction had a significant influence on recommendation intention and revisit intention. Third, consumer intention to revisit specialized coffee shops showed a significant influence on recommendation intention.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.320-325
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• 2002

Today many companies offer millions of products to customers. They are faced with a problem to choose particular products . In response to this problem a new marking strategy, recommendation has emerged. Among recommendation technologies collaborative filtering is most preferred. But the performance degrades with the number of customers and products. Namely, collaborative filtering has two major limitations, sparsity and scalability. To overcome these problems we introduced a new recommendation methodology using adjusted product hierarchy, grain. This methodology focuses on dimensionality reduction to improve recommendation quality and uses a marketer's specific knowledge or experience. In addition, it uses a new measure in the neighborhood formation step which is the most important one in recommendation process.

• 대한설비관리학회지
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• 제23권4호
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• pp.57-64
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• 2018

User-based collaborative filtering is a method of recommending an item to a user based on the preference of the neighbor users who have similar purchasing history to the target user. User-based collaborative filtering is based on the fact that users are strongly influenced by the opinions of other users with similar interests. Item-based collaborative filtering is a method of recommending an item by comparing the similarity of the user's previously preferred items. In this study, we create a recommendation model using user-based collaborative filtering and item-based collaborative filtering with consumer's consumption data. Collaborative filtering is performed by using RFM (recency, frequency, and monetary) technique with purchasing data to recommend items with high purchase potential. We compared the performance of the recommendation system with the purchase amount and the performance when applying the RFM method. The performance of recommendation system using RFM technique is better.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2004년도 추계학술대회 논문집
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• pp.42-42
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• 2004

• 한국멀티미디어학회논문지
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• 제18권9호
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• pp.1068-1082
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• 2015

Due to the rapid advancement of the mobile technology, smart phones have been widely used in the current society. This lead to an easier way to retrieve video contents using web and mobile services. However, it is not a trivial problem to retrieve particular video contents based on users' specific preferences. The current movie recommendation system is based on the users' preference information. However, this system does not consider any emotional means or perspectives in each movie, which results in the dissatisfaction of user's emotional requirements. In order to address users' preferences and emotional requirements, this research proposes a movie recommendation technology to represent a movie's emotion and its associations. The proposed approach contains the development of emotion ontology by representing the relationship between the emotion and the concepts which cause emotional effects. Based on the current movie metadata ontology, this research also developed movie-emotion ontology based on the representation of the metadata related to the emotion. The proposed movie recommendation method recommends the movie by using movie-emotion ontology based on the emotion knowledge. Using this proposed approach, the user will be able to get the list of movies based on their preferences and emotional requirements.

• 아태비즈니스연구
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• 제13권2호
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• pp.169-182
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• 2022

Purpose - The purpose of this study was to investigate influence relationship among health lifestyle, customer satisfaction, repurchase intention and recommendation intention in the protein beverage market. Design/methodology/approach - This study collected 286 survey data from customers who had experience buying and drinking the protein beverage. The Exploratory Factor Analysis (EFA) and the multiple regression analysis were hired in order to analyze the data. Findings - First, four dimensions of health lifestyle("health confidence," "health sensitivity," "health intention," and "health eating habit") were found to be valid and reliable. Second, all four dimensions of health lifestyle had a positive effect on customer satisfaction. Third, customer satisfaction had a positive effect on repurchase intention. Lastly, customer satisfaction had a positive effect on recommendation intention. Research implications or Originality - This study provided research model among health lifestyle, customer satisfaction, repurchase intention and recommendation. Furthermore, the results of this study were useful for identifying the role of health lifestyle in estimating customer satisfaction and the strategies for strengthening customer satisfaction in the protein beverage market.

• 한국식생활문화학회지
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• 제39권1호
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• pp.1-12
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• 2024

Food is essential for sustenance and reflects a country's identity, making it crucial to identify the cultural needs for effectively localizing Korean food. This study surveyed 825 adults from four continents (eight countries) to examine their preferences, familiarity, and attitudes toward Korean food. Significant correlations(p< .001) were found between the familiarity and preference for Korean food, with variations observed across continents. Among the representative Korean food items, the average preference score was 4.67, and the purchase/recommendation intention score was 4.88. Seven items received above-average ratings (e.g., gogi-deopbap and kimchi-bokkeumbap), while some items showed high liking but low purchase/recommendation intention (e.g. dak-jjim and galbi-jjim). In addition, items such as gimbap and tteokbokki had high purchase/recommendation intention but low liking, and kimchi and vegetable foods etc. received low liking and purchase/recommendation intentions. In terms of the preferred meat according to the cooking method and seasoning, beef respondents preferred grilled·stir-fried and soup·stew·hot pot cooking methods, while pork or chicken respondents preferred grilled·stir-fried and frying methods. Soy sauce was the most preferred seasoning for all meat responses, followed by red pepper paste. These research findings provide fundamental data for developing Korean food products, segmented by continent.

• 경영정보학연구
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• 제23권3호
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• pp.51-75
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• 2021

세계적인 전자상거래 기업들은 지속 가능한 경쟁력을 확보하기 위해 사용자 맞춤형 추천 서비스를 제공하고 있다. 기존 관련 연구에서는 주로 평점, 구매 여부 등 정량적 선호도 정보를 사용하여 개인화 추천 서비스를 제공하였다. 하지만 이와 같은 정량적 선호도 정보를 사용하여 개인화 추천 서비스를 제공하면 추천 성능이 저하될 수 있다는 문제점이 제기되고 있다. 호텔을 이용한 사용자가 호텔 서비스, 청결 상태 등에 대하여 만족하지 못한다고 리뷰를 작성하였으나 선호도 평점 5점을 부여했을 때 정량적 선호도(평점)와 정성적 선호도(리뷰)가 불일치한 문제가 발생할 수 있다. 따라서 본 연구에서는 정량적 선호도 정보와 정성적 선호도 정보가 일치하는지를 확인하고 이를 바탕으로 선호도 정보가 일치하는 사용자를 바탕으로 새로운 프로파일을 구축하여 개인화 추천 서비스를 제공하고자 한다. 리뷰에서 정성적 선호도를 추출하기 위해 자연어 처리 관련 연구에서 널리 사용되고 있는 CNN, LSTM, CNN + LSTM 등 딥러닝 기법을 사용하여 감성분석 모델을 구축하였다. 이를 통해 사용자가 작성한 리뷰에서 정성적 선호도 정보를 정교하게 추출하여 정량적 선호도 정보와 비교하였다. 본 연구에서 제안한 추천 방법론의 성능을 평가하기 위해 세계 최대 여행 플랫폼 TripAdvisor에서 실제 호텔을 이용한 사용자 선호도 정보를 수집하여 사용하였다. 실험 결과 본 연구에서 제안한 추천 방법론이 기존의 정량적 선호도만을 고려하는 추천 방법론보다 우수한 추천 성능을 나타냄을 확인할 수 있었다.

• 한국조리학회지
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• 제14권3호
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• pp.109-122
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• 2008

This study aims to examine the properties of using bakeries based on demographic variables and repurchase intention and recommendation intention according to participation rates in promotional events. First, demographic characteristics and frequency of using bakeries were analyzed. Second, crosstabulation analysis on usability was conducted according to demographic variables. Third, effects of participation rates in promotional events on repurchase intention and recommendation intention were examined. This study could provide the reasons why bakeries should do promotional events, showing their effects on sales. Surveys were conducted with the students of the culinary department and bakery customers to investigate the influence on repurchase intention and recommendation intention according to participation rates in promotional events. As a result, both repurchase intention and recommendation intention showed a significant result. Also, the study aims to improve understanding by analyzing demographic variables on the properties of using bakeries(frequency in use of bakeries, purchase type, bakery type that is preferred, a time slot being made a purchase, objects of purchase, and purchasing price per person) and create data to help the management of a bakery.