• International journal of advanced smart convergence
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• v.9 no.3
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• pp.42-48
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• 2020

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

• 한국IT서비스학회:학술대회논문집
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• 2005.11a
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• pp.356-365
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• 2005

Since their first inception a few decades ago, software components have received much attention mainly due to their alleged benefits of quality and productivity improvement. Despite this, it is yet to be agreed upon what and how components should be designed. This paper aimed to bridge the gap by proposing a collaborative process where the voice of the customer is captured and documented by employing the event and entity models. These requirement elements (WHAT) are cross-tabulated in three relation matrices in accordance with the weights provided by the business users. The requirements are fed into the algorithm invented by the authors to optimize the component grouping (HOW). This collaborative process has been successfully validated at an enterprise wide software development project. The process was effective to help the users more actively involved in the design of the system and made the whole process faster and more adaptive to the changes.

• The Journal of the Korea Contents Association
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• v.7 no.11
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• pp.23-33
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• 2007

The collaborative filtering has used the nearest neighborhood method based on the preference and the similarity using the Pearson correlation coefficient. Therefore, it does not reflect content of the items and has the problems of the sparsity and scalability as well. the item-based collaborative filtering has been practically used to improve these defects, but it still does not reflect attributes of the item. In this paper, we propose the user and item based collaborative filtering using the classification property and Naive Bayesian to supplement the defects in the existing recommendation system. The proposed method complexity refers to the item similarity based on explicit data and the user similarity based on implicit data for handing the sparse problem. It applies to the Naive Bayesian to the result of reference. Also, it can enhance the accuracy as computation of the item similarity reflects on the correlative rank among the classification property to reflect attributes.

• 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 Korea Multimedia Society
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• v.20 no.2
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• pp.336-343
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• 2017

We propose a real time personalized recommendation algorithm on the mobile device. We use a unified collaborative filtering with reduced data. We use Fuzzy C-means clustering to obtain the reduced data and Konohen SOM is applied to get initial values of the cluster centers. The proposed algorithm overcomes data sparsity since it extends data to the similar users and similar items. Also, it enables real time service on the mobile device since it reduces computing time by data clustering. Applying the suggested algorithm to the MovieLens data, we show that the suggested algorithm has reasonable performance in comparison with collaborative filtering. We developed Android-based smart-phone application, which recommends restaurants with coupons and restaurant information.

• Journal of Information Technology Services
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• v.6 no.2
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• pp.113-123
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• 2007

In ubiquitous computing environment such as ubiquitous marketplace (u-market), there is a need of providing context-based personalization service while considering the nomadic user preference and corresponding requirements. To do so, the recommendation systems should deal with the tremendous amount of context data. Hence, the purpose of this paper is to propose a novel recommendation method which provides the products-group list of the customers in u-market based on the shopping intention and preferences. We have developed FREPIRS(FREquent Purchased Item-sets Recommendation Service), which makes recommendation listof product-group, not individual product. Collaborative filtering and apriori algorithm are adopted in FREPIRS to build product-group.

• Journal of Ship and Ocean Technology
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• v.8 no.4
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• pp.45-55
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• 2004

How to achieve engineering collaboration among diverse engineering activities is one of the key topics in manufacturing fields nowadays. The infrastructure for collaborative engineering is essential, and it can be realized by information technologies and intelligent engineering applications in digital environments. Digital Manufacturing is a technology to facilitate effective product developments and agile productions by computer models representing the physical and logical schema and the behavior of real manufacturing systems including products, processes and factories. A digital factory as a well-designed and integrated digital environment is incorporated in it. In this paper, digital manufacturing is recommended as a good strategy for collaborative engineering, especially in product developments and productions. By business process analysis and some case studies, we suggested sophisticated digital models are very useful to concurrent and collaborative engineering. It is expected that digital manufacturing is a very good strategy for achieving dramatic time and cost savings in many engineering activities of many manufacturing industries, including machinery, automotive and shipbuilding.

• Proceedings of the Korea Association of Information Systems Conference
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• 2005.12a
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• pp.463-470
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• 2005

In a rapidly changing business environment, the improvement of managerial techniques through IT utilization brings about remarkable increases in profitability and redesign of work process for better performances. IT innovation by electronic instruments such as ICT e-business provides accelerates forming inter-organizational information network and helps them benchmark the best practices of advanced organizations. A new shift of paradigm by e-business across all enterprises has turned the traditional aspects of inter-organizational competition and relationship into a form of collaboration. Collaboration enables business activities in parallel position among companies and facilitates cooperation between partner enterprises. Lately, the concept of 'Synchronization' is emerging beyond dimension of cooperation between networks, and the most concepts related to it are converging into 'Collaboration Networks'. This research observes a virtual organization as a form of collaborative networks, and measures the degree of virtualization of Korean collaborative organizations.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.239-250
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• 2003

Recent recommender system user a method of combining collaborative filtering system and content based filtering system in order to slove the problem of the Sparsity and First-Rater in collaborative filtering system. In this paper, to make up for the prediction accuracy in hybrid Recommender system, the harmonic mean weight(CBCF_harmonic_mean) is used for calculating the user similarity weight. After setting up the threshold as 45 considering the performance of content based filtering, we apply significance weight of n/45 to user similarity weight. To estimate the performance of the proposed method, it if compared with that of combing both the existing collaborative filtering system and the content- based filtering system. As a result, it confirms that the suggested method is efficient at improving the prediction accuracy as solving problems of the exiting collaborative filtering system.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2723-2733
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• 2023

Transuranic nuclides (such as ²³⁸Pu, ²⁵²Cf, ²⁴⁹Bk, etc.) have a wide range of application in industry, medicine, agriculture, and other fields. However, due to the complex conversion chain and remarkable fission losses in the process of transuranic nuclides production, the generation amounts are extremely low. High flux reactor with high neutron flux and flexible irradiation channels, is regarded as the promising candidate for producing transuranic nuclides. It is of great significance to increase the conversion ratio of transuranic nuclides, resulting in higher efficiency and better economy. In this paper, we perform an optimization design evaluation of producing transuranic nuclides in high flux reactor, which includes optimization design of irradiation target and influence study of reactor core loading. It is demonstrated that the production rate increases with appropriately determined target material and target structure. The target loading scheme in the irradiation channel also has a significant influence on the production of transuranic nuclides.