• Asia pacific journal of information systems
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• v.29 no.4
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• pp.856-874
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• 2019

The performance of recommender systems (RS) has been measured mainly in terms of accuracy. However, there are other aspects of performance that are difficult to understand in terms of accuracy, such as coverage, serendipity, and satisfaction with recommended results. Moreover, particularly with RSs that suggest multiple items at a time, such as YouTube, user satisfaction with recommended results may vary not only depending on their accuracy, but also on their configuration, content, and design displayed to the user. This is true when classifying an RS as a single RS with one recommended result and as a multiple RS with diverse results. No empirical analysis has been conducted on the influence of the content and distribution of recommendation items on user satisfaction. In this study, we propose a research model representing the content and distribution of recommended items and how they affect user satisfaction with the RS. We focus on RSs that recommend multiple items. We performed an empirical analysis involving 149 YouTube users. The results suggest that user satisfaction with recommended results is significantly affected according to the HHI (Herfindahl-Hirschman Index). In addition, satisfaction significantly increased when the recommended item on the top of the list was the same category in terms of content that users were currently watching. Particularly when the purpose of using RS is hedonic, not utilitarian, the results showed greater satisfaction when the number of views of the recommended items was evenly distributed. However, other characteristics of selected content, such as view count and playback time, had relatively less impact on satisfaction with recommended items. To the best of our knowledge, this study is the first to show that the category concentration of items impacts user satisfaction on websites recommending diverse items in different categories using a content-based filtering system, such as YouTube. In addition, our use of the HHI index, which has been extensively used in economics research, to show the distributional characteristics of recommended items, is also unique. The HHI for categories of recommended items was useful in explaining user satisfaction.

• Information Systems Review
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• v.12 no.3
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• pp.19-48
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• 2010

This study focused on characterizing various factors in order for learners to maintain their interests in learning and to maximize learning effects as the top priority purpose of university e-Learning, on the basis of results of conceptual studies on existing e-Learning and practical studies, and then on examining them practically. It also analyzed which factors would have greater influence on learning effects of e-Learning in general. Moreover, in comparison with existing numerous studies which examined only factor such as learning effects of e-Learning, it analyzed such things in detail according to division into three items such as learning satisfaction, learning transfer and learning recommendation. To achieve such purposes of the study, it characterized and set 3 factors such as learning contents, instructional design and user convenience on the assumption that such factors have a significant influence on learning effects of e-Learning. Moreover, the factor of learning contents includes 3 detailed elements, i.e., learning issue and objective, knowledge information, and consistency and propriety, and the factor of instructional design includes 4 detailed elements, i.e., interest and sympathy, interaction, contents presentation and explanatory strategy. Lastly, the factor of user convenience includes 2 detailed elements such as screen configuration, and check-up of contents and teaching schedule. According to analytical results, it showed all 3 factors such as learning contents, instructional design and user convenience have a significant influence on learning effects of e-Learning(i.e., learning satisfaction, learning transfer and learning recommendation). In more detail, it showed the learning issue and objective from the factor of learning contents have the greatest influence on learning satisfaction of e-Learning. Then, it is the most important to set the learning issue and objective with given priority to learners and set the learning objective estimable, in order to raise the learning satisfaction. It showed the contents presentation from the factor of instructional design on the learning transfer. Therefore, it is the most important to structuralize mutual relation and presentation orders to promote learning systematically and to let learners access to such things, for the purpose of raising the learning transfer. Moreover, it showed the interest and sympathy from the factor of instructional design has the greatest influence on the learning recommendation. Thus, it is the most important to promote learners' interests to the maximum using well-timed media, and to give a lecture enough to arouse learners' sympathy.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Smart Media Journal
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• v.12 no.8
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• pp.9-17
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• 2023

The development of information and communication technology and the improvement of the development and dissemination of smart devices have caused changes in the form of tourism, and the concept of smart tourism has since emerged. In this regard, researches related to smart tourism has been conducted in various fields such as policy implementation and surveys, but there is a lack of research on application reviews. This study collects Trip Advisor application review data in the Google Play Store to identify usage of the application and user satisfaction through Latent Dirichlet Allocation (LDA) topic modeling. The analysis results in four topics, two of which are positive and the other two are negative. We found that users were satisfied with the application's recommendation system, but were dissatisfied when the filters they set during search were not applied or that reviews were not published after updates of the application. We suggest more categories can be added to the application to provide users with different experiences. In addition, it is expected that user satisfaction can be improved by identifying problems within the application, including the filter function, and checking the application environment and resolving the error occurring during the application usage.

• Information Systems Review
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• v.22 no.2
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• pp.25-41
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• 2020

Along with the expectation about 5G network commercialization, interests in realistic and immersive media industries such as virtual reality (VR) are increasing. However, most of studies on VR still focus on video technologies instead of factors for popularity and consumption. Thus, the main objective of this research is to identify meaningful factors, which affect the view counts of VR videos and to provide business implications of the content strategies for VR video creators and service providers. Using a regression analysis with 700 VR videos, this study tries to find major factors that affect the view counts of VR videos. As a result, user assessment factors such as number of likes and sicknesses have a strong influence on the view counts. In addition, the result shows that both general information factors (video length and age) and content characteristic factors (series, one source multi use (OSMU), and category) are all influential factors. The findings suggest that it is necessary to support recommendation and curation based on user assessments for increasing popularity and diffusion of VR video streaming.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of the Korean Society of Radiology
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• v.10 no.8
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• pp.619-627
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• 2016

The purpose of this study is to investigate the effect of CT contrast agent and MRI contrast agent on the area dose in the body when using automatic exposure control system in general radiography. After making rectangular holes in the center of the abdominal thickness paraffin phantom, CT contrast agent and MRI contrast agent were respectively diluted with physiological saline solution for contrast medium dilution ratio of 10:0, 9:1, 8:2, 7:3, 6:4, 5:5, 4:6, 3:7, 2:8, 1:9, 0:10%. Each experiment was set to 78 kVp, 320 mA, which is the proper condition for KUB photography, and thereafter a total of 30 inspections were made for each dilution ratio using an automatic exposure control device, and the area dose corresponding to the dilution ratio of each contrast agent, Average comparison and correlation analysis were performed on the exposure index. As a result, the CT contrast agent and the MRI contrast agent appeared different in area dose according to the dilution ratio(p<0.05), and as the dilution ratio increased, the area dose increased for CT contrast agent and MRI contrast agent(P<0.05). In each test, the exposure index showed the manufacturer's recommendation of 200-800 EI value, and the exposure index and area dose increased as the area dose increased(p<0.05). In conclusion, CT contrast agent and MRI contrast agent confirmed to increase the area dose by general imaging test using all automatic exposure control device. Therefore, it is considered that it is necessary to perform it after the contrast medium has been excreted sufficiently when using usual imaging test after using the contrast agent in CT and MRI examination.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.29-39
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• 2020

Online communities, such as Internet portal sites and social media, have become popular since they allow users to share opinions and to obtain information anytime, anywhere. Accordingly, an increasing number of opinions are manipulated to the advantage of particular groups or individuals, and these opinions include falsified product reviews and political propaganda. Existing detection systems are built upon the characteristics of manipulated opinions for one particular time period. However, manipulation tactics change over time to evade detection systems and to more efficiently spread information, so detection systems should also evolve according to the changes. We therefore propose a system that helps observe and trace changes in manipulation tactics. This system classifies opinions into clusters that represent different tactics, and changes in these clusters reveal evolving tactics. We evaluated the system with over a million opinions collected during three election campaigns and found various changes in (i) the times when manipulations frequently occur, (ii) the methods to manipulate recommendation counts, and (iii) the use of multiple user IDs. We suggest that the operators of online communities perform regular audits with the proposed system to identify evolutions and to adjust detection systems.

• Korean Journal of Health Education and Promotion
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• v.17 no.1
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• pp.131-154
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• 2000

This study was conducted to provide basic data for development of public information program and public policy which could prevent the medicine abuse in Korea, examining the level of knowledge of medicine and its related factors. Data were collected from the 2,011 residents who live in mtropolitan cities, large-sized cities, small and medium cities, and small towns The results of this study are summarized as follows. 1) In case of purchasing of medicines in pharmacy, 67.3％ of the respondents chose the medicines through recommendations of the professionals such as pharmacists and doctors, while 32.7％ of the respondents chose the medicine through self-judgement, advertizing, or recommendation of relative. 2) 64.7％ of the respondents obtained the information on medicines through TV. It appeared to be higher in the groups of female of the twenties, the unmarred, a brother and sister threesome, highschool graduates, housewives, residents in small and medium cities, atheists, and the middle class, displaying the significant difference from the other groups. 3) 40.5％ of the respondents recognized the side effect of the medicine when they took the medicine, while 34.4％ did not recognize it. The rate of experience in the side effect was 39.7％. The informations on the medicine abuse and the risk of addiction were obtained through broadcast media (47.9％), publications (12.1％), and health professionals (11.6％). 4) 81.1％ of the respondents experienced taking of the fatigue relieving medicine. The experience in taking of the fatigue relieving medicine appeared to be higher in the groups of the forties. the married. a brother and sister threesome. highschool graduates. persons engaging in farming, livestock raising, and forestry, the residents in small towns, and Christians. Each group displayed the significant difference from the other groups. 5) According to the level of knowledge of medicines, the respondents marked average 11.7 ± 3.76 points on the base of 24 points. It appeared to be higher in the groups of female of the twenties, a brother and sister foursome, college graduates, teachers, Catholics, and the middle class, displays the significant difference from the other groups. 6) According to the experience in taking of health medicine and health food, 81.1％ of respondents had the experience in taking ‘the fatigue relieving medicine’; 72.4％ ‘carrot or vegetable juice’; 69.5％ ‘ginseng’; 63.0％ ‘mushroom’; 42.5％ ‘dog meat’; 38.0％ ‘aloe’; 36.4 ‘deer antlers’; 11.4％ ‘snake’; 2.0％ ‘the penis of a fur seal’. 7) The factors influencing the level of knowledge of medicine include experiences in taking of the tonic, the fatigue relieving medicine, and the nutritive medicine, economic status, the number of brothers and sisters, education level, marital status, father's education level, and age. The factors influencing the experience in side effect of medicine are experiences in taking of the fatigue relieving medicine, the nutritive medicine, and the tonic, sex, age, education level, father's education level, marital status, economic status, religion, and the number of brothers and sisters. In conclusion, it is estimated that the level of knowledge of medicines is significantly low in Korea. Especially, it is found out that workmen, students, the upper class, the class of low education level, and persons engaging in farming, livestock raising, and forestry neglect importance of knowledge of medicine. Therefore, it is necessary for public authority, associations related, and health professionals to develop programs for public information and education to help people obtain basic knowledge of medicine.

• Korean Journal of Poultry Science
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• v.22 no.3
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• pp.167-178
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• 1995

The purpose of this research was to get basic information for the development of Korean native chicken industry by reviewing the current native chicken farm management and marketing situation of native chicken products(meat and eggs). The research was carried on the basis of the farm field survey covering 210 native chicken feeders out of 9 different local areas, and the results were as follows ; 1. Average raising size of native chicken flocks of sample farms was 1,787 heads and about 50% of those farms raised less than 500 heads chickens for self-sufficiency or on the side. 2. Most farmers made the decision to start on feeding native chickens in small scale with small amount of capital without sound feeding program, and their decision was mainly influenced by recommendation of mass-media( 19.5%) and neighbors (17.2%). 3. The average income per farm earned by raising the native chickens was 13,719 Won, and income per head of chicken was 8,800 Won. 4. About 40% of feeders expressed that the poor marketing management and lack of capital were the bottleneck to native chicken farm management. 5. About 70% of feeders evaluated the prospect of native chicken industry positively and so, about 60% of feeders hoped to expand the raising size in the future. 6. Most farmers directry made a bargain with marketer including middleman and enduser in selling the chicken products because there was not established special marketing system for native chicken products. 7. The sales age of native broiler was about 16~20 weeks and average body weight of broiler was 1.5~2.0 kg. And farm recieved price was not decided on the basis of each body weight or meat quality but only number of heads. 8. The average first egg-laying age of chickens was about 165 days and average annual laying rate was only about 56%. 9. In order to develop the successful Korean native chicken industry, followings are recommended ; 1) Reducing the production costs and increasing the productivity of native chickens should be carried out through technological research and development for sound feeding program of native chickens and sufficient fund supply. 2) Orderly native chicken marketing and pricing system should be established to give good vision about native chickens to farmers and to delight the consumers. 3) The measures for product differentiation including meat quality and nutritional value of native chicken products against other improved chickens should be actively taken by feeders and government.