• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.316-322
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• 2014

A movie recommendation system is proposed to learn a preference model of a viewer by using multimodal features of a video content and their evoked implicit responses of the viewer in synchronized manner. In this system, facial expression, body posture, and physiological signals are measured to estimate the affective states of the viewer, in accordance with the stimuli consisting of low-level and affective features from video, audio, and text streams. Experimental results show that it is possible to predict arousal response, which is measured by electrodermal activity, of a viewer from auditory and text features in a video stimuli, for estimating interestingness on the video.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.207-215
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• 2011

Due to the increasing availability and popularity of digital television (DTV), the numbers of TV channels and programs that can be selected by consumers are also increasing rapidly. Therefore, searching for interesting channels and program via remote controls or channel guide maps can be frustrating and slow. In this paper, in order to better satisfy consumers, we propose a dynamic channel surfing scheme that reduces the channel seek distance in DTV. The proposed scheme dynamically rearranges the channel sequences according to the channel currently being watched to reduce the channel seek distance. The results of a simulation experiment demonstrate that the proposed dynamic channel surfing scheme reduces the channel seek distance for DTV channel navigation when up-down channel selection interfaces are used.

• The Journal of KAIRB
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• v.3 no.1
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• pp.11-18
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• 2021

Purpose: The purpose of this study is to investigate a satisfaction survey of untact education and platforms that can be used for untact education to provide recommendations on future development of Education of Persons Conducting Clinical Trials. Methods: Online survey was distributed among students who have taken Untact Education of Persons Conducting Clinical Trials. The result was separated according to topic and descriptive statistics was used for analysis. The satisfaction survey used 10-point scale. Results: Of the 1,720 students who received the survey, 1,347 (78.3%) responded to the lecture satisfaction survey. The satisfaction level for broadcasting program (Kakao TV), an untact educational platform for the education of clinical trial workers at Kyung Hee University Medical Center, was relatively high with 8.09±1.99 points. Average score respondents recommending Kyung Hee University Untact Education of Persons Conducting Clinical Trials was 8.03±1.83 and customer recommendation score (Net Promotor Score) was 27.1%. Satisfaction level of the preferred training time was divided into weekday-morning (8-11 AM) (8.16±1.75), weekday-afternoon (12-4 PM) (7.73±2.07), weekday-evening (5-9 PM) (7.78±2.22), and weekend-morning (9-11 AM) real-time untact education (8.48±1.76) and analyzed. There was a noticeable difference between weekend-morning and weekday-afternoon (p<0.0001) and weekend-morning and weekday-evening (p=0.0001) real-time untact education. When asked about conducting education after COVID-19 pandemic ends, 79.2% (1,012 of 1,279) of the respondents answered that they prefer real-time untact education while 20.8 % (266 of 1,279) preferred face-to-face education. Conclusion: Online education, without time and space constraint, is expected to be the mainstream market in Korea for Education of Persons Conducting Clinical. Kyung Hee University Untact Education of Persons Conducting Clinical has achieved above average satisfaction using Kakao TV. Kyung Hee University Real-time Untact Education of Persons Conducting Clinical Net Promotor Score is 27.1%, which is above industry average, communication with trainees should be considered to improve Net Promotor Score.

• 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.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.183-187
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• 2017

Smart phone users prefer fast reading and texting. Hence, users frequently use abbreviated sequences of words and phrases. Nowadays, abbreviations are widely used from chat terms to technical terms. Therefore, gathering abbreviations would be helpful to many services, including information retrieval, recommendation system, and so on. However, manually gathering abbreviations needs to much effort and cost. This is because new abbreviations are continuously generated whenever a new material such as a TV program or a phenomenon is made. Thus it is required to generate of abbreviations automatically. To generate Korean abbreviations, the existing methods use the rule-based approach. The rule-based approach has limitations, in that it is unable to generate irregular abbreviations. Another problem is to decide the correct abbreviation among candidate abbreviations generated rules. To address the limitations, we propose a method of generating Korean abbreviations automatically using sequence-to-sequence learning in this paper. The sequence-to-sequence learning can generate irregular abbreviation and does not lead to the problem of deciding correct abbreviation among candidate abbreviations. Accordingly, it is suitable for generating Korean abbreviations. To evaluate the proposed method, we use dataset of two type. As experimental results, we prove that our method is effective for irregular abbreviations.

• 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.