• Journal of the Korean Society for Library and Information Science
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• v.54 no.4
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• pp.147-167
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• 2020

The purpose of this study is to analyze the factors influencing the intention of revisiting and recommending by applying a structural equation model, targeting the service quality factors of university libraries derived from previous studies. For 11 days from April 30th, 2020 to May 10th, 2020, a total of 127 user groups (undergraduate students, graduate students, professors/instructors) were surveyed on their intention to revisit and recommend. The analysis results are as follows. 'Materials' and 'service customization' were shown as quality dimensions that influence revisit. In addition, revisiting was found to have an effect on recommendation intention, and it was analyzed that 'materials' and 'service customization' affect not only revisit but also recommendation intention. In addition, 'service customization' was found to be a factor that directly affects the intention to recommend. Based on this, a method of applying the concept of customization to library services and marketing was proposed in an environment where users' needs are diversifying and becoming personalized.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1183-1190
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• 2017

With the development of network and IT technology, people are searching and purchasing items they want, not bounded by places. Therefore, there are various studies on how to solve the scalability problem due to the rapidly increasing data in the recommendation system. In this paper, we propose an item-based collaborative filtering method using Tag weight and a recommendation technique using MapReduce method, which is a distributed parallel processing method. In order to improve speed and efficiency, the proposed method classifies items into categories in the preprocessing and groups according to the number of nodes. In each distributed node, data is processed by going through Map-Reduce step 4 times. In order to recommend better items to users, item tag weight is used in the similarity calculation. The experiment result indicated that the proposed method has been more enhanced the appropriacy compared to item-based method, and run efficiently on the large amounts of data.

• 한국상하수도협회지
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• s.27
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• pp.8-9
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• 2009

정부는 6월 8일 4대강 살리기 프로젝트의 마스터플랜을 최종 확정했다. 마스터플랜은 4대강 인근 12개 시 도를 대상으로 지역설명회, 관계부처 학회 등의 추천을 받은 전문가 그룹의 자문, 물환경학회 수자원학회 등 간련학회의 토론, 전문가와 시만이 참여한 공청회를 통해 각계의 다양한 의견을 수렴하여 최종 확정하게 되었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.83-88
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• 2013

This paper proposes a recommending system of the similar blogs gathered with similarities between blogs according to the similarity, dividing words, for each frequency, that individual blogs have. It improved the algorithm of k-means, using the model of the habits of white ants for better performance of clustering, and showed better performance of clustering as a result of evaluating and comparing with the existing algorithm of k-means as the improved algorithm. The recommending system of similar blog was designed and embodied, using the improved algorithm. TCA can reduce clustering time and the number of moving time for clustering compare with K-means algorithm.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.106-114
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• 2019

The study was conducted to investigate the effect of whole-wheat ratio and nutrition information provision on purchasing behavior and consumer intention among individuals in their twenties who lack calcium intake. In the end, this study aims to provide basic data on the development and marketing strategy of customized nutrition-reinforced snacks. Regarding whole wheat ratios, the acceptance of taste of whole wheat flour was highest in ZF but didn't seem significant difference with HF's. The familiarity of taste and flavor (P <0.001), purchase intention (p <0.001) and recommendation intention (p <0.001) were higher in order of ZF, HF and TF. Regarding information provision, familiarity of taste and flavor (P <0.05), purchase intention (p <0.05) and recommendation intention (p <0.05) were higher in order of detailed information group, non - information group and simple information group. Therefore, developing calcium-fortified biscuit, mixing whole wheat flour with normal flour might reduce consumer's resistance, Also, providing detailed information on the degree of fortification of calcium and dietary fiber might cause a synergistic effect on consumption.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.515-526
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• 2019

In the real world, the user's preference for a particular product is determined by many factors besides the quality of the product. The reflection of these external factors was very difficult because of various fundamental problems including lack of data. However, access to external factors has become easier as the infrastructure for public data is opened and the availability of evaluation platforms with diverse and vast amounts of data. In accordance with these changes, this paper proposes a recommendation system structure that can reflect the collectable factors that affect user's preference, and we try to observe the influence of actual influencing factors on preference by applying case. The structure of the proposed system can be divided into a process of selecting and extracting influencing factors, a process of supplementing insufficient data using sentence analysis, and finally a process of combining and merging user's evaluation data and influencing factors. We also propose a validation process that can determine the appropriateness of the setting of the structural variables such as the selection of the influence factors through comparison between the result group of the proposed system and the actual user preference group.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.249-254
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• 2013

The number of channels are increased into several hundreds of channels when coming out the digital broadcasting era. In this environment, viewers searching for programs will be very difficult to do. In addition, recent popularization of smart devices are receiving the services that they previously had not been given to. A TV program recommended a system that has been studied as a way to solve these problems. However, most studies have been studied in most web-based research results when applied to broadcast TV for TV program recommendations. In particular, the combination of the current members who watch TV are not considered. In this paper, the environment and TV viewers are considering a combination of the members of the TV program's recommended system proposal. In order to make a group deal successful, we employ the face recognition.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.60-69
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• 2017

Recently, with the rapid development of internet and smart phones, social network services that can create and share various information through relationships among users have been actively used. Especially as the amount of information becomes enormous and unreliable information increases, expert recommendation that can offer necessary information to users have been studied. In this paper, we propose an expert recommendation scheme considering users' interests, human relations, and response quality. The users' interests are evaluated by analyzing their past activities in social network. The human relations are evaluated by extracting the users who have the same interesting fields. The response quality is evaluated by considering the user's response speed and response contents. The proposed scheme determines the user's expert score by combining the users' interests, the human relations, and the response quality. Finally, we recommend proper experts by matching queries and expert groups. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of The Korean Association of Information Education
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• v.11 no.3
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• pp.281-288
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• 2007

Collaborative filtering is a method to predict preference items of a user based on the evaluations of items provided by others with similar preferences. Collaborative filtering helps general people make smart decisions in today's information society where information can be easily accumulated and analyzed. We designed, implemented, and evaluated a course recommendation system experimentally. This system can help university students choose courses they prefer to. Firstly, the system needs to collect the course preferences from students and store in a database. Users showing similar preference patterns are considered into similar groups. We use Pearson correlation as a similarity measure. We select K-nearest students to predict the unknown preferences of the student and provide a ranked list of courses based on the course preferences of K-nearest students. We evaluated the accuracy of the recommendation by computing the mean absolute errors of predictions using a survey on the course preferences of students.

• Journal of Information Technology and Architecture
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• v.10 no.3
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• pp.371-378
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• 2013

Video-demand learning through E-learning continuously increases on these days. However, not all video-demand learning systems can be utilized properly. When students study by education videos not matched to level of their own, it is possible for them to lose interest in learning. It causes to reduce the learning efficiency. In order to solve the problem, we need to develop a recommendation system which recommends customized education videos according the study levels of students. In this paper, we estimate the study level based on the history of students' watching behaviors such as average watching time, skipping and rewinding of videos. In the experimental section, we demonstrate our recommendation system using real students' video watching history to show that our system is feasible in a practical environment.