• Journal of Korea Multimedia Society
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• v.22 no.2
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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.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.276-279
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• 2004

The location-based service is one of the most powerful services in the mobile area. The location-based service provides information service for moving user's location information and information service using wire / wireless communication. In this paper, we propose a model for personalized recommendation system which includes location information and personalized recommendation system for location-based service. For this service system, we consider mobile clients that have a limited resource and low bandwidth. Because it is difficult to input the words at mobile device, we must deliberate it when we design the interface of system. We design and implement the personalized recommendation system for location-based services(advertisement, discount news, and event information) that support user's needs and location information. As a result, it can be used to design the other location-based service systems related to user's location information in mobile environment. In this case, we need to establish formal definition of moving objects and their temporal pattern.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.40-51
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• 2011

The key issues of recommendation systems provide the contents satisfying the interests of users for the huge amounts of contents over internet. The existing recommendation system use the algorithms considering the users' profiles and context information to enhance the exactness of a recommendation. However, the existing recommendation system can't satisfy the requirements of service providers because the business models of service providers is not considered. In this paper, we propose the mobile recommendation system using the composite contexts and the recommendation weights applying the business model of service providers. The proposed system retrieves the contents of the contents providers using composite context information and apply the recommendation weights to recommend the suitable contents for the business models of service providers. Therefore, we provide the contents satisfying the consumption value of users and the business models of service providers to mobile users.

• Journal of the Korea Fashion and Costume Design Association
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• v.14 no.4
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• pp.43-61
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• 2012

This research aims to examine the impact of store personality and service quality on the customers' intention of revisiting the department store and their intention of recommendation to others. The participants were women in their 20s to 50s with experiences of purchasing apparel from major department stores. A total of 324 survey responses were used for the final analysis. The data were analyzed using factors analysis, reliability analysis, and multiple regression analysis with PASW 18.0. The results were as follows. First, the department store personality was composed of 3 factors; prestige, passion, sincerity. Service quality factors were defined as tangibility, responsiveness, and empathy. Second, the three dimensions of brand personality-prestige, passion and sincerity turned out to be influential factors affecting the customers' revisiting intention and recommendation intention. Also, tangibility and responsiveness of service quality factors had a significant influence on their revisiting intention, whereas tangibility, responsiveness and empathy factors had a significant influence on their recommendation intention. Third, the sub-dimensions of store personality and service quality had a different influence on the customers' revisiting intention and recommendation intention according to the department store brand.

• Journal of Korean Society of Rural Planning
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• v.24 no.1
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• pp.89-97
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• 2018

Recently, interest in rural tourism for urban dwellers has increased, and rural communities are chosen as tourist destinations. Under these circumstances, the study was designed to analyze the effects of the quality of service at rural stay sites on customer satisfaction and recommendation intention. The analysis method analyzes the demographic characteristics of the survey participants and characteristics of participation in rural stay. And the quality of service for the experience of rural stay was analyzed with SERVQUAL'S five-dimensional type, reliability, assurance, responsiveness, empathy, tangible and customer satisfaction, intent of recommendation and regression. Major analysis shows that the survey subjects were found to have an average age of 41.8 years, 49 to 59 years old, and a high degree of university graduation. And as characteristic of participation, the form of company was family and relatives, the form of family meeting was many summer, the reservation was Internet, and payment by cash and card were many. As a result of the hypothesis testing, reliability, assurance, responsiveness, and empathy among the quality of service of rural stay were affected in customer satisfaction. In addition, the quality of service and the intent to recommend it were statistically significant, reliability, assurance and empathy. Therefore, it is deemed necessary to make efforts to improve service quality as the quality of service at rural stay places has relevance to customer satisfaction and recommendation intention.

• Journal of the Ergonomics Society of Korea
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• v.23 no.3
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• pp.1-12
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• 2004

In recent years, with the advent of e-Commerce the need for personalized services and one-to-one marketing has been emphasized. To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. In this paper, we provide an intelligent agent approach to incorporate human sensibility into an one-to-one recommendation service in cyber shopping mall. Our system exploits human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. Customizing the system`s behavior requires the parallel execution of several tasks during the interaction （e. g., identifying the customer`s emotional preference and dynamically generating the pages of the store catalog）. The recommendation agent system composed of five modules including specialized agents carries on these tasks. By presenting goods that are consistent with user interests as well as user sensibility, the accuracy and satisfaction of the recommendation service may be improved.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.1
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• pp.119-126
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• 2009

In recent years, many recommendation service systems that search or recommend information automatically considering user's taste or property are developed. However, there is a weak point that correct recommendation is hard without considering the preference of user's context. This paper proposes a filtering method that gives correct recommendation considering the preference of user's context. To support this method, we get UCOP(User-Context Object Preference) using the preference of user's context and Pearson correlation coefficient. The results of the experiment show the improvement of 11%, 2% of precision and 8%, 4% of recall comparing with the existing service systems. Our recommendation service systems show 77% of precision and 53% of recall overall.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.110-115
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• 2006

In pure P2P environment, it is possible to provide service by using a little real-time information without using accumulated information. But in case of using only a little information that was locally collected, quality of recommendation service can be fallen-off. Therefore, it is necessary to study a method to improve qualify of recommendation service by using users' context information. But because a great volume of users' context information can be recognized in a moment, there can be a scalability problem and there are limitations in supporting differentiated services according to fields and items. In this paper, we solved the scalability problem by clustering context information per each service field and classifying it per each user, using SOM. In addition, we could recommend proper services for users by quantifying the context information of the users belonging to the similar classification to the service requester among classified data and then using collaborative filtering.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.81-90
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• 2012

For digital TV, the recommendation of u-health personalized service of semantic environment should be done after evaluating individual physical condition, illness and health condition. The existing recommendation method of u-health personalized service of semantic environment had low user satisfaction because its recommendation was dependent on ontology for analyzing significance. We propose the personalized service recommendation method based on Naive Bayesian Classifier for u-health service of semantic environment in digital TV. In accordance with the proposed method, the condition data is inferred by using ontology, and the transaction is saved. By applying naive bayesian classifier that uses preference information, the service is provided after inferring based on user preference information and transaction formed from ontology. The service inferred based on naive bayesian classifier shows higher precision and recall ratio of the contents recommendation rather than the existing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2743-2762
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• 2014

Promoted by cloud technology and new websites, plenty and variety of Web services are emerging in the Internet. Meanwhile some Web services become outdated even obsolete due to new versions, and a normal phenomenon is that some services work well only with other services of older versions. These laggard or improper services are lowering the performance of the composite service they involved in. In addition, using current technology to identify proper semantic services for a composite service is time-consuming and inaccurate. Thus, we proposed a clustering method and a recommendation method to deal with these problems. Clustering technology is used to classify semantic services according to their topics, functionality and other aspects from plenty of services. Recommendation technology is used to predict the possible preference of a composite service, and recommend possible component services to the composite service according to the history information of invocations and similar composite services. The experiments show that our clustering method with the help of Ontology and TF/IDF technology is more accurate than others, and our recommendation method has less average error than others in the series of missing rate.