• Journal of the Korea Convergence Society
• /
• v.7 no.6
• /
• pp.29-34
• /
• 2016

Nowadays, real-time devices that provide health care for a companion animal is being developed by IoT technology and its demand such as smart puppy tag is increasing. However, it is difficult for IoT devices of companion animals to process complex nature due to miniaturized hardware and constructive nature. There is a clear limit to custom advanced features like health care implementation. This paper designs an integrated platform with statistical analysis which makes it possible to customized services such as feed production, pharmaceutical production, and health care for each companion animal. Middleware that collects sensor information, customer's spending pattern and information from Social Network Service is also designed by making use of IoT devices which companion animals wear. Furthermore, the paper designed data analyzer which analyzes and refines data from collected information that can be applied to personalized services.

• Journal of Korean Library and Information Science Society
• /
• v.24
• /
• pp.301-334
• /
• 1996

This study is aimed at examination and analysis of current status of university library's line and staff organization especially the private university's. Motivation for this study is based on the following facts : First, the library work now is changing from the conventional one to automated one. Second, librarians in the front line must be trained in the newly set work because of the separation of work process in automated libraries. Methodologies of this study, apart from theoretical aspects, were visiting and interviewing librarians at 30 university libraries, examining the current status and problems of the line and staff organization of the university libraries. The result from the study is as follows: First, interviewing reveals that 35% of the 75 private university library is having 'associate directorship by librarian' system. Benefits from this system are described, recommending other University libraries adopt this system. This system su n.0, pplements the weak point of concurrent director's office of lay professor, as well as encourages librarian's morale by promotion. Second, the current organization of the university libraries are to be reformed. Namely, 1) the name of each division must be newly and a n.0, ppropriately set of changed suiting for the work of automated library, thus reforming the division. This must be conducted via collection of opinions of the Korean Library and Information Science Society' and associated organizations. 2) Newly formed division(for example, administrative division or division of operation and management, division conducting digital library work, etc.) must be added to the line and staff organization. 3) For information service division, there must be a certain number of subject specialists. 4) Status of the directorship of university library, librarianship, issue of renaming of university library are also described.

• Journal of Broadcast Engineering
• /
• v.16 no.2
• /
• pp.237-246
• /
• 2011

In order to produce the variety of UCC, the recommendation service is required which considers the copyright of UCC producer discriminated from one for UCC consumers and the purpose of its production. The recommender system designed in this thesis enables UCC which is much similar to one UCC producer utilizes to be used with custom-made when recommending and producing based on UCC view history and production list, etc. of its producer. The recommender system is largely divided into filtering based on the preferred tag, UCC filtering used when producing the preferred UCC and creating process of recommended UCC using the Pearson formula. The recommender system in this thesis requires the data which were used when producing UCC. For that, we added the reference factor so that the data of UCC which were utilized when producing UCC into the existing metadata can be recorded. If the recommender system suggested in this thesis is used, the more effective and convenient UCC recommendation services with custom-made for producers can be provided.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.2
• /
• pp.224-229
• /
• 2011

Collaborative filtering is used the most for recommendation systems because it can recommend potential items. However, when there are not many items to be evaluated, collaborative filtering can be subject to the influence of similarity or preference depending on the situation or the whim of the evaluator. In addition, by recommending items only on the basis of similarity with items that have been evaluated previously without relation to the present situation of the user, the recommendations become less accurate. In this paper, in order to solve the above problems, before starting the collaborative filtering procedure, we calculated similarity not by comparing all the values evaluated by users but rather by comparing only those users who were above the average in order to improve the accuracy of the recommendations. In addition, in the ceaselessly changing ubiquitous computing environment, it is not proper to recommend service information based only on the items evaluated by users. Therefore, we used methods of calculating similarity wherein the users' real time context information was used and a high weight was assigned to similar users. Such methods improved the recommendation accuracy by 16.2% on average.

• Journal of Digital Convergence
• /
• v.11 no.3
• /
• pp.345-352
• /
• 2013

The demand and production for mobile music increases as the number of smart phone users increase. Thus, the standard of selection of a user's preferred music has gotten more diverse and complicated as the range of popular music has gotten wider. Research to find intelligent techniques to ingeniously recommend music on user preferences under mobile environment is actively being conducted. However, existing music recommendation systems do not consider and reflect users' preferences due to recommendations simply employing users' listening log. This paper suggests a personalized music-recommending system that well reflects users' preferences. Using AHP, it is possible to identify the musical preferences of every user. The user feedback based on the Bayesian network was applied to reflect continuous user's preference. The experiment was carried out among 12 participants (four groups with three persons for each group), resulting in a 87.5% satisfaction level.

• Journal of Digital Convergence
• /
• v.11 no.3
• /
• pp.389-395
• /
• 2013

More sales of food and beverage coupons have been made using SNS on social commerce recently. If one buys coupons on social commerce, he/she can enjoy products at a lower price; however, there are drawbacks that one must consider such as location, service hours, and discount rate. Thus, this paper suggests a system that recommends food and beverage coupons on social commerce for users that considers a user's personal context of location, time, and purchase history. In order to reflect a user's context awareness and continuous preference, this paper suggests a method based on the Bayesian network. In order to reflect personalized weighting on the standard of coupon selection to match a user's preference, a measurement and classification of weighting preferences is performed on the basis of AHP. 20 experiments in one month involving 12 students were carried out to verify the effectiveness of the system, resulting in an 80% satisfaction level.

• Journal of Fashion Business
• /
• v.15 no.6
• /
• pp.1-22
• /
• 2011

This paper is a study on the information required for developing Korean clothing products intended for Chinese students in Korea and for opening markets of Korean clothing and brands in China. It analyses the buying behaviors, purchasing ability, the favourite apparel type for clothing, and satisfaction with Korean clothing and brands of Chinese students in Korea, with which it seeks a program for South Korea branding to enter into the Chinese clothing market. Three hundred fifty seven students of Hannam University and PaiChai University Chung nam National University in Daejeon-city took part in this study. This paper adopts Descriptive Analysis, Crossing Analysis, Bivariate Correlations, and One-way ANOVA in SPSS 17.0 with Post Hoc Multiple Comparisons to know about the impact of demographic variables of Chinese students in Korea on buying information sources, the criteria for store selection, buying capacity, praise degree on various properties of Korean clothes products and their satisfaction with Korean clothes products. The first proposal of expanding China market for Korean merchants is to achieve maximum sales based on sales promotion strategies, such as the credit card corporations, the store display and sales person service development, SPA, design size development, and to upgrade consumption values. The second proposal is Korean clothes corporations should open the Internet shopping corresponding to the physical stores, the most frequently used information source of Chinese students is the network, from the age distribution of Internet users in 2008 in China, population above 10 and below 30 accounts for 66.7% of all users, In recommending clothes made in Korea to Chinese young people, on-line advertising will get better effects than other strategies, specially during advertisement, they should take good use of Korean television shows and variety shows or help Chinese poor areas to do the social contribution hereby to improve the public image of Korean clothes corporations, which can bring good sale promotion effects as well.

• Journal of the Korea Convergence Society
• /
• v.9 no.12
• /
• pp.39-45
• /
• 2018

Twitter users use 'Following', 'Retweet' and so on to find tweets that they are interested in. However, it is difficult for users to find tweets that are of interest to them on Twitter, which has more than 300 million users. In this paper, we developed a customized tweet recommendation system to resolve it. First, we gather current trends to collect tweets that are worth recommending to users and popular tweets that talk about trends. Later, to analyze users and recommend customized tweets, the users' tweets and the collected tweets are categorized. Finally, using Web service, we recommend tweets that match with user categorization and users whose interests match. Consequentially, we recommended 67.2% of proper tweet.

• Journal of Information Technology Services
• /
• v.21 no.2
• /
• pp.109-126
• /
• 2022

The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.6
• /
• pp.1-12
• /
• 2022

As the music streaming service and the Hi-Fi market grow, various audio devices are being released. As a result, consumers have a wider range of product choices, but it has become more difficult to find products that match their musical tastes. In this study, we proposed a system that extracts the vocal component from the user's preferred sound source and recommends the most suitable audio device to the user based on this information. To achieve this, first, the original sound source was separated using Python's Spleeter Library, the vocal sound source was extracted, and the result of collecting frequency band data of manufacturers' audio devices was shown in a grid graph. The Matching Gap Index (MGI) was proposed as an indicator for comparing the frequency band of the extracted vocal sound source and the measurement data of the frequency band of the audio devices. Based on the calculated MGI value, the audio device with the highest similarity with the user's preference is recommended. The recommendation results were verified using equalizer data for each genre provided by sound professional companies.