• 한국식품영양학회지
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• 제24권4호
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• pp.670-679
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• 2011

This study investigated the hotel buffet restaurant's service quality, emotional reaction of customer, recommendation intention, and defection intention. The survey was conducted from January 3 to February 7 in 2011, and 400 respondents were used in the data analysis. As a results of this study, the hotel buffet restaurant's service quality was classified by the interaction, outcome, and physical environment quality. The emotional reaction of hotel buffet restaurant's customer was classified by the positive and negative emotion. The all factors of hotel buffet restaurant's service quality had a positive impact on positive emotion, while it had a negative impact on negative emotion. The positive emotion reaction of hotel buffet restaurant's customer had a positive impact on the recommendation intention, while the negative emotion had a negative impact on the recommendation intention. And the negative emotion had a positive impact on the defection intention in hotel buffet restaurants. In addition, there were partially differences in the service quality and emotional reaction by general characteristics. There were significant differences in the recommendation intention by marriage status and monthly income. Therefore, the hotel buffet restaurants have to design a strategy of service for increasing customer's positive emotion and recommendation intention.

• 한국멀티미디어학회논문지
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• 제22권2호
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• pp.260-270
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• 2019

As the growth of developing Islamic countries, Muslims are into the world. The most important thing for Muslims to purchase food, ingredient, cosmetics and other products are whether they were certified as 'Halal'. With the increasing number of Muslim tourists and residents in Korea, Halal restaurants and markets are on the rise. However, the service that provides information on Halal restaurants and markets in Korea is very limited. Especially, the application of recommendation system technology is effective to provide Halal restaurant information to users efficiently. The profiling of Halal restaurant information should be preceded by design of recommendation system, and design of recommendation algorithm is most important part in designing recommendation system. In this paper, an Intelligent Recommendation Service system for offering Halal food (IRSH) based on dynamic profiles was proposed. The proposed system recommend a customized Halal restaurant, and proposed recommendation algorithm uses hybrid filtering which is combined by content-based filtering, collaborative filtering and location-based filtering. The proposed algorithm combines several filtering techniques in order to improve the accuracy of recommendation by complementing the various problems of each filtering. The experiment of performance evaluation for comparing with existed restaurant recommendation system was proceeded, and result that proposed IRSH increase recommendation accuracy using Halal contents was deducted.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 춘계학술발표대회
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• pp.389-392
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• 2020

The growth in social media, blogs and restaurant listing directories have led to increasing customer reviews about restaurants, their quality of food items and services available on the internet. These user reviews offer a massive amount of valuable information that can be used for various decision-making purposes. Currently, most food recommendation sites provide recommendation scores about restaurants rather than food items of the restaurant and the provided recommendation scores may be biased since they are calculated only from user reviews listed only in their sites. Usually, people wants a reliable recommendation about foods, not restaurant. In this paper, we present a reliable Korean food items rating method; we first extract food items by applying NER technique to restaurant reviews collected from many Korean restaurant recommendation web sites, blogs and web data. Then, we apply lexicon-based sentiment analysis on collected user reviews and predict people's opinions as sentiment polarity scores (+1 for positive; -1 for negative; 0 for neutral). Finally, by taking average of all calculated polarity scores about a food item, we obtain a rating to individual menu items of the restaurant. The proposed food item rating is more reliable since it does not depend on reviews of only one site.

• 한국멀티미디어학회논문지
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• 제14권1호
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• pp.112-120
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• 2011

본 연구에서는 상황 적용 시스템을 이용하여 위치 인식 기반의 음식점 추천 서비스를 개발함으로써 이 시스템이 실제 상황 인식 응용 프로그램 개발에도 유용하게 사용될 수 있음을 보일 것이다. 이를 위해 상황 적응 시스템을 기반으로 하여 사용자의 위치 선호도와 검색 히스토리 등의 정보를 이용하는 위치 인식 기반의 맞춤형 음식점 추천 응용 프로그램을 개발하였다. 상황 적용 시스템은 개발자가 작성한 정책 파일의 내용에 따라 변화된 상황에 맞도록 응용 프로그램을 자동적으로 적응시키고, 응용 프로그램은 위치 등과 같은 변화된 상황을 기반으로 음식점 추천 서비스를 제공한다.

• 한국식생활문화학회지
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• 제21권4호
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• pp.396-405
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• 2006

The purpose of this study was to analyze the effect of the consumer-brand relationship quality on revisit intent and recommendation intent in the family restaurant. The questionnaires were distributed to 320 students in the K University located in Masan, who were sampled by convenience-sampling method. The surveys were conducted from November,10 to 24,2005. The 287 questionnaires were responded, and 15 unusable questionnaires were excluded, then 272 were used for the final analysis(response rate: 85.0%). The result of this study showed that 3 constructs(self-connective attachment, satisfaction and intimacy) of consumer-brand relationship quality have significant effects on the revisit intent(p<.01) and 2 constructs(satisfaction and intimacy) of consumer-brand relationship quality had significant positive effects on the recommendation intent in the family restaurant(p<.01) It meant that as consumer-brand relationship quality became stronger, the customer's revisit intent and recommendation intent became greater. As a conclusion, the foodservice manager in the family restaurant should focus on the marketing strategy to strengthen the quality of consumer-brand relationship especially emphasizing on satisfaction and intimacy in order to increase the revisit intent and recommendation intent of customers.

• International Journal of Advanced Culture Technology
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• 제8권4호
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• pp.263-270
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• 2020

The growth of the mobile app markets has made it popular among people who recommend relevant information about restaurants. The recommendation service app based on AI Chatbot is that it can efficiently manage time and finances by making it easy for restaurant consumers to easily access the information they want anytime, anywhere. Eating out consumers use smartphone applications for finding restaurants, making reservations, and getting reviews and how to use them. In addition, social attention has recently been focused on the research of AI chatbot. The Chatbot is combined with the mobile messenger platform and enabling various services due to the text-type interactive service. It also helps users to find the services and data that they need information tersely. Applying this to restaurant recommendation services will increase the reliability of the information in providing personal information. In this paper, an artificial intelligence chatbot-based smartphone restaurant recommendation app using personalization information is proposed. The recommendation service app utilizes personalization information such as gender, age, interests, occupation, search records, visit records, wish lists, reviews, and real-time location information. Users can get recommendations for restaurants that fir their purpose through chatting using AI chatbot. Furthermore, it is possible to check real-time information about restaurants, make reservations, and write reviews. The proposed app uses a collaborative filtering recommendation system, and users receive information on dining out using artificial intelligence chatbots. Through chatbots, users can receive customized services using personal information while minimizing time and space limitations.

• International Journal of Contents
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• 제5권2호
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• pp.20-25
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• 2009

The purpose of this study is to seek new u-business services in restaurant management. Using the concept of business model methodology in family restaurant management domain, this study identifies customers' needs in services at the stage of management of purchase of materials, the production management, and the sales management. In addition, this study suggests two killer applications of a family restaurant management linking with the latest ubiquitous computing technologies: the service of the customer-oriented menu recommendation and the service of the inventory-oriented menu recommendation. These findings may offer practical insights in the context of ubiquitous service model of restaurant management.

• 한국조리학회지
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• 제23권6호
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• pp.27-35
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• 2017

The purpose of this study was to examine the causal relationship among restaurant selection attributes and customer satisfaction and recommendation tastes for young people in their twenties who use tickets for Rail-ro. Data collection was conducted to utilize questionnaire survey with online and offline distribution. The collected data were analyzed using a statistical program SPSS 21.0 with frequency analysis, reliability analysis, factor analysis, and regression analysis. The results of the study showed that Internet search is the most common source of information about restaurants during the trip, and restaurant choice attributes have an important impact on customer satisfaction, food quality, employee service and reputation, but hygiene did not have a big effect on customer satisfaction. In addition, customer satisfaction has a significant effect on recommendation intention. Concluding the results from this study, it investigated the significant attributes for customers selection of restaurants and provide meaningful advice for market managers to make useful marketing strategies to attract more clients and augment economic benefits.

• 경영정보학연구
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• 제25권1호
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• pp.27-46
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• 2023

최근에는 외식 산업의 발달과 레스토랑 수요의 증가로 인해 레스토랑 추천 시스템 연구가 활발하게 제안되고 있다. 기존 레스토랑 추천 시스템 연구는 정량적인 평점 정보 또는 온라인 리뷰의 감성분석을 통해 소비자의 선호도 정보를 추출하였는데 이는 소비자의 의미론적 선호도 정보는 반영하지 못한다는 한계가 존재한다. 또한, 레스토랑이 포함하는 세부적인 속성을 반영한 추천 시스템 연구는 부족한 실정이다. 이를 해결하기 위해 본 연구에서는 소비자의 선호도와 레스토랑 속성 간의 상호작용을 효과적으로 학습할 수 있는 딥러닝 기반 모델을 제안하였다. 먼저, 합성곱 신경망을 온라인 리뷰에 적용하여 소비자의 의미론적 선호도 정보를 추출했고, 레스토랑 정보에 임베딩 기법을 적용하여 레스토랑의 세부적인 속성을 추출했다. 최종적으로 요소별 연산을 통해 소비자 선호도와 레스토랑 속성 간의 상호작용을 학습하여 소비자의 선호도 평점을 예측했다. 본 연구에서 제안한 모델의 추천 성능을 평가하기 위해 Yelp.com의 온라인 리뷰를 사용한 실험 결과, 기존 연구의 다양한 모델과 비교했을때 본 연구의 제안 모델이 우수한 추천 성능을 보이는 것을 확인하였다. 본 연구는 레스토랑 산업의 빅데이터를 활용한 맞춤형 레스토랑 추천 시스템을 제안함으로써 레스토랑 연구 분야와 온라인 서비스 제공자에게 학술적 및 실무적 측면에서 다양한 시사점을 제공할 수 있을 것으로 기대한다.

• 디지털융복합연구
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• 제19권5호
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• pp.187-196
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• 2021

교통약자는 우리 사회의 높은 비율을 차지하고 있는 대표적인 사회 취약계층이다. 최근 기술의 발달로 사회취약 계층을 위한 맞춤형 복지 기술이 연구되고 있으나, 일반인들과 비교하면 상대적으로 부족한 실정이다. 이에 본 연구에서는 교통약자를 위한 맞춤형 식당 추천시스템을 구현하고자 한다. 이를 위해 특별교통수단 승하차 이력(7,153건), 대구 푸드 식당 상세정보(955건)의 자료를 결합하여 하이브리드 추천시스템을 구현하였다. 구현된 추천시스템의 유효성 평가를 위해 예측 오차율, 추천 커버리지로 기존 추천시스템들과 성능 비교를 수행하여 유효성을 검증하였다. 분석 결과 기존 추천시스템보다 높은 성능으로 나타났으며, 교통약자를 위한 맞춤형 식당 추천시스템의 가능성을 확인하였다. 또한 일부 교통약자 유형에서 유사한 식당이 추천되는 상관성을 확인하였다. 본 연구결과는 교통약자들의 만족도 높은 식당 이용에 기여할 것으로 판단되며, 연구의 한계점 또한 제시하였다.