• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.443-454
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• 2019

In recent years, there has been active research on a recommender system that considers three or more inputs in addition to users and goods, making it a multi-dimensional array, also known as a tensor. The main issue with using tensor is that there are a lot of missing values, making it sparse. In order to solve this, the tensor can be shrunk using the tensor decomposition algorithm into a lower dimensional array called a factor matrix. Then, the tensor is reconstructed by calculating factor matrices to fill original empty cells with predicted values. This is called tensor reconstruction. In this paper, we propose a user-based Top-K recommender system by normalized PARAFAC tensor reconstruction. This method involves factorization of a tensor into factor matrices and reconstructs the tensor again. Before decomposition, the original tensor is normalized based on each dimension to reduce overfitting. Using the real world dataset, this paper shows the processing of a large amount of data and implements a recommender system based on Apache Spark. In addition, this study has confirmed that the recommender performance is improved through normalization of the tensor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1708-1734
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• 2022

In the development of logistics system, the breakthrough of important technologies such as technology platform for logistics information management and control is the key content of the study. Based on Javascript and JQuery, the logistics system realizes real-time monitoring, collection of historical status data, statistical analysis and display, intelligent recommendation and other functions. In order to strengthen the cooperation of warehouse storage, enhance the utilization rate of resources, and achieve the purpose of real-time and visual supervision of transportation equipment and cargo tracking, this paper studies the VNF dynamic deployment and SFC routing problem in the network load change scenario based on the logistics system. The BIP model is used to model the VNF dynamic deployment and routing problem. The optimization objective is to minimize the total cost overhead generated by each SFCR. Furthermore, the application of the SFC mapping algorithm in the routing topology solving problem is proposed. Based on the concept of relative cost and the idea of topology transformation, the SFC-map algorithm can efficiently complete the dynamic deployment of VNF and the routing calculation of SFC by using multi-layer graph. In the simulation platform based on the logistics system, the proposed algorithm is compared with VNF-DRA algorithm and Provision Traffic algorithm in the network receiving rate, throughput, path end-to-end delay, deployment number, running time and utilization rate. According to the test results, it is verified that the test results of the optimization algorithm in this paper are obviously improved compared with the comparison method, and it has higher practical application and promotion value.

• KIEAE Journal
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• v.6 no.3
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• pp.43-48
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• 2006

This paper aims to suggest ways of institutionally improving standards that must be applied when installing Water Reclamation/Reuse (WRR) system based on efficiency analysis. Currently, the standard for WRR system establishment requires that the system should treat more than 10% of used water in the building of over $60,000m^2$ in total area of all floors, but our research has found that it would be more effective to change the standard to $150-m^3-per-day$ reclaimed water or the total area of all floors of $30,000m^2$ ($50,000m^2$ in the case of an office building). In other words, what this paper suggests is not a one-size-fits-all standard based on the total area of all floors, but a reasonable and flexible standard that takes into account efficiency and a unit water usage according to a building's purpose. Furthermore, this paper recommends a new WRR standard that can be applied to large-scale land development for housinglots, like the New Town. The recommendation is based on the economic analysis that the WRR system will ensure efficiency only if the amount of reclaimed water is over 4,000 tons per day, which corresponds to 4 millions square meters of housinglots. Regarding the size of the established facility, this paper suggests changing the standard, which is now set at over 10% of water usage, to what is relative to the total amount of use of reclaimed water in order to ensure efficiency and promote use of reclaimed water. In addition, this paper proposes that governmental support should be offered not only to facility owners, who are recipients at present, but also to facility builders. By doing so, those who donate a facility to the government, central or local, after building it, can be provided with substantial aid. Therefore, the application of the institutional improvement suggested in this paper is expected to create environment-friendly living conditions and boost the quality of life by encouraging people to secure water resources efficiently in buildings, and in a wider range, in cities.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• The Korean Journal of Air & Space Law and Policy
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• v.31 no.2
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• pp.215-236
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• 2016

This paper is related to a study on safety management of cosmic radiation in the aviation area, and as a comprehensive study encompassing not only aviation crew but also aviation traffic users, presents issues on an exposure to the cosmic radiation which authors predict may be intensified in a time to come. Although the government of the Republic of Korea has recently activated regulations related to the cosmic radiation, the following improvement measures are further urged to be carried out not only as a regulatory improvement for pushing ahead with effectiveness but also as a supplementary tool. Firstly, a dose limit corresponding to the international standard needs to be applied. Since the dose limit imposed by the Korean government is improperly higher than the international dose limit of the cosmic radiation, the present dose limit needs to be re-established in a range of "not exceeding the international recommendation". Secondly, a new methodology is needed such that aviation companies observe a yearly effective dose limit of passengers. A fact that only aviation crew is specified but passengers are excluded in the related regulation is based on a recommendation presented by the International Commission on Radiological Protection (ICRP). According to the recommendation, Korean government excluded passengers in the "Cosmic Radiation Safety Requirements for Crew". Among the present aviation regulations, there exists a protection standard for protecting aviation traffic users. However, it presents a damage protection only for ticket-related issues. Since this regulatory weakness provides a cause of endangering national health, the authors believe that an improvement in the regulation is needed without sticking to the recommendation from the ICRP. To this end, new regulations are strongly demanded from aspects of not only legal but also regulatory areas. The dose limit in accordance with the international standard is established. However, at least a minute amount of cosmic radiation is continuously acting on all people of Korea. Since more and higher level of cosmic ration may exist in the aviation space, an improved method of representing the minute amount of cosmic radiation in figures. As a result, a desirable regulation may be established for protecting not only crew but also aviation traffic users from being exposed to the cosmic radiation via a legislation of the desirable regulation.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.163-172
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• 2012

Collaborative filtering which is used explicit method in a existing recommedation system, can not only reflect exact attributes of item but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. This paper proposes the personalized recommendation system using RFM method and k-means clustering in u-commerce which is required by real time accessablity and agility. In this paper, using a implicit method which is is not used complicated query processing of the request and the response for rating, it is necessary for us to keep the analysis of RFM method and k-means clustering to be able to reflect attributes of the item in order to find the items with high purchasablity. The proposed makes the task of clustering to apply the variable of featured vector for the customer's information and calculating of the preference by each item category based on purchase history data, is able to recommend the items with efficiency. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.129-139
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• 2012

This paper introduces a recommender system that is to recommend broadcast content. Our recommender system uses user's viewing history for personalized recommendations. Broadcast contents has unique characteristics as compared with books, musics and movies. There are two types of broadcast content, a series program and an episode program. The series program is comprised of several programs that deal with the same topic or story. Meanwhile, the episode program covers a variety of topics. Each program of those has different topic in general. Therefore, our recommender system recommends TV programs to users according to the type of broadcast content. The recommendations in this system are based on user's viewing history that is used to calculate content similarity between contents. Content similarity is calculated by exploiting collaborative filtering algorithm. Our recommender system uses java sparse array structure and performs memory-based processing. And then the results of processing are stored as an index structure. Our recommender system provides recommendation items through OPEN APIs that utilize the HTTP Protocol. Finally, this paper introduces the implementation of our recommender system and our web demo.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.401-404
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• 2010

Researches on prodigies and education for those have recently been progressing in many fields. Education for the gifted, which was basically on Math and Science on the start, now includes Intelligence, Invention, Cultural Sciences, Art, and so on. With the progression towards extremely developed information society, interests in and importance on the courses for the talented get more and more focused. The problem is, however, choosing the gifted and educating them is not an easy matter, since the history of Intelligence Technology is relatively short and it is hard to identify prodigies and categorize what kinds of courses they need. Also, from 2010 "Science Education Institute for the Gifted" freshmen draft, paper-based admission test has been discarded and teacher-recommendation through long-term observation introduced. Therefore needs have been increasing for quality selection methods including observation records, recommendation letters, and portfolios. Reformation on teaching and creative selection methods has been accentuated because of lack of academic base for selecting candidates for education for the gifted. Because of all those mentioned above, reliances for the selection processes during the last three years and the one in 2010, observation records, recommendations and portfolios included, have been analyzed and evaluated. Several factors which can be used instead of paper-based tests were coordinated. Based on it, it was highly possible and has been successful to draft all the applicants in cognitive, sentimental, and creative fields.

• Journal of Science and Technology Studies
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• v.4 no.1 s.7
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• pp.59-82
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• 2004

Nanotechnology is a rapidly expanding field, focused on the creation of functional materials, devices, and systems through the control of matter on the nanometer scale. Recently many countries including Korea are rushing into promoting research and development of nanotechnology. Because the nanoscale is not just other step toward miniaturization, but a qualitatively new scale, progress in nanotechnology will have very far-reaching social, ethical, and environmental impacts. This paper aims to examine social issues and implications of nanotechnology development. To do so, this paper divides the issues around nanotechnology into several sub-issues: environmental, health-related, and societal issues. And then this paper reviews the debates and disputes around those sub-issues. Based on this review, this paper proposes some policy recommendation.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.89-96
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• 2020

Linked Data allows structured data to be published in a standard way that datasets from various domains can be interlinked. With the rapid evolution of Linked Open Data(LOD), researchers are exploiting it to solve particular problems such as semantic similarity assessment. In this paper, we propose a method, on top of the basic concept of Linked Data Semantic Distance (LDSD), for calculating the Linked Data semantic distance between resources that can be used in the LOD-based recommender system. The semantic distance measurement model proposed in this paper is based on a similarity measurement that combines the LOD-based semantic distance and a new link weight using TF-IDF, which is well known in the field of information retrieval. In order to verify the effectiveness of this paper's approach, performance was evaluated in the context of an LOD-based recommendation system using mixed data of DBpedia and MovieLens. Experimental results show that the proposed method shows higher accuracy compared to other similar methods. In addition, it contributed to the improvement of the accuracy of the recommender system by expanding the range of semantic distance calculation.