• Journal of Information Technology Applications and Management
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• v.26 no.5
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• pp.57-65
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• 2019

Recommendation Systems are information technologies that E-commerce merchants have adopted so that online shoppers can receive suggestions on items that might be interesting or complementing to their purchased items. These systems stipulate valuable assistance to the user's purchasing decisions, and provide quality of push service. Traditionally, Recommendation Systems have been designed using a centralized system, but information service is growing vast with a rapid and strong scalability. The next generation of information technology such as Cloud Computing and Big Data Environment has handled massive data and is able to support enormous processing power. Nevertheless, analytic technologies are lacking the different capabilities when processing big data. Accordingly, we are trying to design a conceptual service model with a proposed new algorithm and user adaptation on dynamic recommendation service for big data environment.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.165-170
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• 2007

Recommendation systems provide users with proper services using context information being input from many sensors occasionally under ubiquitous computing environment. But in case there isn't sufficient context information for service recommendation in spite of much context information, there can be problems of resulting in inexact result. In addition, in the quantification step to use context information, there are problems of classifying context information inexactly because of using an absolute classification course. In this paper, we solved the problem of lack of necessary context information for service recommendation by using dynamic profile information. We also improved the problem of absolute classification by using a relative classification of context information in quantification step. As the result of experiments, expectation preference degree was improved by 7.5% as compared with collaborative filtering methods using an absolute quantification method where context information of P2P mobile agent is used.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.111-117
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• 2020

Dessert Ateliers (DA) are small shops that sell high-end homemade desserts such as macaroons, cakes, and cookies, and their popularity is increasing according to the emergence of small luxury trends. Even though each DA sells the same kinds of desserts, they are differentiated by the personality of their pastry chef; thus, there is a need to purchase desserts online that customers cannot see and purchase offline, and thus dessert e-commerce has emerged. However, it is impossible for customers to identify all the information of each DA and clearly understand customers' preferences when buying desserts through the dessert e-commerce. When a dessert e-commerce service provides a DA recommendation service, customers can reduce the time they hesitate before making a decision. Therefore, this paper proposes two kinds of DA recommendation method: a clustering-based recommendation method that calculates the similarity between customers' content and DAs and a dynamic weighting-based recommendation method that trains the importance of decision factors considering customer preferences. Various experiments were conducted using a real-world dataset to evaluate the performance of the proposed methods and it showed satisfactory results.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.1-10
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• 2015

We propose and implement a personalized tour recommendation system based on ontology. We utilize user's profile, dynamic information on search in the application, web search, and facebook for personalized recommendation. We construct tour database for England based on ontology for a demo service, and recommend tour spot considering an individual preference with tour database. This dynamic and personalized tour service makes it possible for individual to plan one's own tour by considering recommended tour spots for each individual.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.31-50
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• 2009

As the collected information which is static or dynamic is infinite in ubiquitous computing environments, information overload and invasion of privacy have been pressing issues in the recommendation service. In this study, we propose a recommendation service procedure through P2P, The P2P helps customer to obtain effective and secure product information because of communication among customers who have the similar preference about the products without connection to server. To evaluate the performance of the proposed recommendation service, we utilized real transaction and product data of the Korean mobile company which service character images. We developed a prototype recommender system and demonstrated that the proposed recommendation service makes an effect on recommending product in the ubiquitous environments. We expect that the information overload and invasion of privacy will be solved by the proposed recommendation procedure in ubiquitous environment.

• Journal of KIISE
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• v.44 no.9
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• pp.908-917
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• 2017

As web service technologies are widely used, there have been many efforts to develop approaches for recommending appropriate web services to users in complex and dynamic service environments. In addition, for the effective development of service mashups, service recommender systems that are specialized for service composition have been developed. However, existing service recommender systems for service mashups are not effective at recommending services in a personalized manner that reflect developers' preferences. To deal with this issue, we propose an approach that recommends services based on the similarities between mashup developers who have developed similar service mashups. The proposed approach is then evaluated by using the mashup data retrieved from ProgrammableWeb. The evaluation results clearly show that the proposed approach is an effective way of improving service recommendations compared to the traditional user-based collaborative filtering algorithm.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1709-1718
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• 2006

Users are able to use the information and service more free than previous wire network due to development of wireless network and device. For this reason, various studies on ubiquitous networks have been conducted. Various contexts brought in this ubiquitous environment, have recognized user's action through sensors. This results in the provision of better services. Because services exist in various places in ubiquitous networks, the application has the time of services searching. In addition, user's context is very dynamic, so a method needs to be found to recommend services to user by context. Therefore, techniques for reducing the time of service and increasing accuracy of recommendation are being studied. But it is difficult to quickly and appropriately provide large numbers of services, because only basic context information is stored. For this reason, we suggest DUPS(Dimension User Profile System), which stores location, time, and frequency information of often used services. Because previous technique used to simple information for recommending service without predicting services which is going to use on future, we can provide better service, and improve accuracy over previous techniques.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.49-59
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• 2014

The Internet of Things(IoT) is a new promising technology made from a variety of technology. The IoT links the objects or people, then enabling anytime, anywhere connectivity for anything and not only for anyone. Social networking services have changed the way people communicate. Recently, new research challenges in many areas of Internet of things and social networking services are fired. In this paper, we propose things recommendation method using social relationship in social Internet of Things. We study previous researches about social network service, IoT, and social IoT. We proposed SIoT_FW(Social IoT Friendship Weight) using static and a dynamic social friendship weight. Also, our method considers four social relationships (Ownership Object Relationship, Co-Location Object Relationship, Social Object Relationship, Parental Object Relationship). We presents a music device scenario using our proposed method.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.65-73
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• 2017

Advances in Internet of Things Technology lead to the increasing number of daily-life things that are interconnected over the Internet. Also, several smart services are being developed by utilizing the connected things. Among the daily-life things surrounding user, the mirror can supports broad range of functionality and expandable service as it plays various roles in daily-life. Recently, various smart mirrors have been launched in certain places where people with specific goals and interests meet. However, most mirrors give the user limited information. Therefore, we designed and implemented a smart mirror that can support customized service. The proposed smart mirror utilizes information provided by other existing internet services to give user dynamic information as real_time traffic information, news, schedule, weather, etc. It also supports recommendation service based on user usage information.