• The Korean Journal of Food And Nutrition
• /
• v.24 no.4
• /
• pp.670-679
• /
• 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.

• Journal of Korea Multimedia Society
• /
• v.22 no.2
• /
• pp.260-270
• /
• 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.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.05a
• /
• pp.389-392
• /
• 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.

• Journal of Korea Multimedia Society
• /
• v.14 no.1
• /
• pp.112-120
• /
• 2011

This research aims to show that the context adaptation system can be used to develop practical context-aware applications by developing a restaurant recommendation system based on location-awareness. In this research, we have designed and implemented a location-aware restaurant recommendation system which provides a customized restaurant recommendation service based on the user's current context. The context-adaptation engine adapts the application program according to the policy file as contexts are changed, and the application provides restaurant recommendation service based on the changed context like location.

• Journal of the Korean Society of Food Culture
• /
• v.21 no.4
• /
• pp.396-405
• /
• 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
• /
• v.8 no.4
• /
• pp.263-270
• /
• 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
• /
• v.5 no.2
• /
• pp.20-25
• /
• 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.

• Culinary science and hospitality research
• /
• v.23 no.6
• /
• pp.27-35
• /
• 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.

• Information Systems Review
• /
• v.25 no.1
• /
• pp.27-46
• /
• 2023

Research on restaurant recommender systems has been proposed due to the development of the food service industry and the increasing demand for restaurants. Existing restaurant recommendation studies extracted consumer preference information through quantitative information or online review sensitivity analysis, but there is a limitation that it cannot reflect consumer semantic preference information. In addition, there is a lack of recommendation research that reflects the detailed attributes of restaurants. To solve this problem, this study proposed a model that can learn the interaction between consumer preferences and restaurant attributes by applying deep learning techniques. First, the convolutional neural network was applied to online reviews to extract semantic preference information from consumers, and embedded techniques were applied to restaurant information to extract detailed attributes of restaurants. Finally, the interaction between consumer preference and restaurant attributes was learned through the element-wise products to predict the consumer preference rating. Experiments using an online review of Yelp.com to evaluate the performance of the proposed model in this study confirmed that the proposed model in this study showed excellent recommendation performance. By proposing a customized restaurant recommendation system using big data from the restaurant industry, this study expects to provide various academic and practical implications.

• Journal of Digital Convergence
• /
• v.19 no.5
• /
• pp.187-196
• /
• 2021

The mobility handicapped are representative socially vulnerable people who account for a high percentage of our society. Due to the recent development of technology, personalized welfare technologies for the socially vulnerable are being studied, but it is relatively insufficient compared to the general people. In this study, we intend to implement a personalized restaurant recommendation system for the mobility handicapped. To this end, a hybrid recommendation system was implemented by combining the data of special transportation boarding and alighting history (7,153 cases) and information of Daegu Food restaurants (955 cases). In order to evaluate the effectiveness of the implemented recommendation system, we conducted performance comparisons with existing recommendation systems by prediction error rate and recommendation coverage. As a result of the analysis, the performance was higher than that of the existing recommendation system, and the possibility of a personalized restaurant recommendation system for the mobility handicapped was confirmed. In addition, we also confirmed the correlation in which similar restaurants are recommended in some types of the mobility handicapped. As a result of this study, it is judged that it will contribute to the use of restaurants with high satisfaction for the mobility handicapped, and the limitations of the study are also presented.