• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.57-84
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• 2023

With the development of information and communications technology (ICT) and big data technology, anyone can easily obtain and utilize vast amounts of data through the Internet. Therefore, the capability of selecting high-quality data from a large amount of information is becoming more important than the capability of just collecting them. This trend continues in academia; literature reviews, such as systematic and non-systematic reviews, have been conducted in various research fields to construct a healthy knowledge structure by selecting high-quality research from accumulated research materials. Meanwhile, after the COVID-19 pandemic, remote healthcare services, which have not been agreed upon, are allowed to a limited extent, and new healthcare services such as health recommender systems (HRS) equipped with artificial intelligence (AI) and big data technologies are in the spotlight. Although, in practice, HRS are considered one of the most important technologies to lead the future healthcare industry, literature review on HRS is relatively rare compared to other fields. In addition, although HRS are fields of convergence with a strong interdisciplinary nature, prior literature review studies have mainly applied either systematic or non-systematic review methods; hence, there are limitations in analyzing interactions or dynamic relationships with other research fields. Therefore, in this study, the overall network structure of HRS and surrounding research fields were identified using citation network analysis (CNA). Additionally, in this process, in order to address the problem that the latest papers are underestimated in their citation relationships, the GraphSAGE algorithm was applied. As a result, this study identified 'recommender system', 'wireless & IoT', 'computer vision', and 'text mining' as increasingly important research fields related to HRS research, and confirmed that 'personalization' and 'privacy' are emerging issues in HRS research. The study findings would provide both academic and practical insights into identifying the structure of the HRS research community, examining related research trends, and designing future HRS research directions.

• Journal of Distribution Science
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• v.4 no.2
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• pp.81-106
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• 2006

The value of Its visitors is very important factors on selection of a Spa & Resort. The first detailed purpose of this paper is to analyse the differences of select behaviors of a Spa & Resort according to the types of values of the visitors. The second aim is to conduct a research on the characters of select behaviors of the visitors. The third aim is to analyse the degree of satisfaction of the visitors, re-visitation and the intention of recommendation. The fourth purpose is to provide useful materials on analysis about the values of the visitors at various Spa & Resorts and to trigger dramatic effect of recuperation, relaxation with its visitor's needs met, the maximum of hotel's management profit at Spa & Resort's area and programs to activate the region's economy. Factor Analsis Routine of SPSS Windows Version 10.0 was applied to accomplish the issues of the study. The Applied analysis by research process are as follows; This paper applied Frequency analysis to figure out interviewee's demographic characters and various using types of the visitors, using their experience of visiting, Select influence, Visiting period, Accommodation they use, Accompanyist, Costs, Season, Transportation, The necessary time. This paper showed important correlation between the visitors' select attributes and behaviors after using it, between their personal value and behaviors after using it, between their individual value, motive of use and their select behavior of destinations. In accordance with it, Managers or developer of a Spa & Resort should make a plan after a sufficient review of the visitors' individual value. The visitor's value is changing continuously according to the change of spatial, occasional environment and should be assessed by those changes.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1689-1697
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• 2018

This paper explored how to design amobile application for Korea's traditional markets based on O2O service and data science technologies. In order to cover a broader scope of customers, diversify the ways to sell products, and increase the profits of Korea's traditional markets, the application bridges online customers with offline stores at traditional markets and augments both convenience and accessibility. Beyond the typical face-to-face interactions between customers and sellers at traditional markets, this application offers mobile payments and personalized recommendations of nearby stores and preferable products using Beacon and datascience technologies. Moreover, it offers multi-language support for foreign customers who are not familiar with Korea's traditional markets and the products sold there. In conclusion, using O2O service, which is a rising trend among prevalent platform technologies, this study proposed a new e-commerce model for Korea's traditional markets to promote market expansion.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.1-7
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• 2013

A personalized recommendation system is a recommendation system that recommends goods to users' taste by using an automated information filtering technology. A collaborative filtering method in this technology is a method that discriminates certain types, which represent similar patterns. Thus, it is possible to estimate the pain strength based on the data of the patients who have the past similar types and extract related conditions according to the similarity in classified patients. A representative method using the Pearson correlation coefficient for extracting the similarity weight may represent inexact results as the sample data is small according to the amount of data. Also, it has a disadvantage that it is not possible to fast draw results due to the increase in calculations as a square scale as the sample data is large. In this paper, the excellency of the intelligence pain nursing intervention u-health system implemented by comparing the scale and similarity group of the sample data for extracting significant data is verified through the evaluation of MAE and Raking scoring. Based on the results of this verification, it is possible to present basic data and guidelines of the pain of patients recognized by nurses and that leads to improve the welfare of patients.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.235-242
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• 2013

Reputation systems aggregate users' feedback after the completion of a transaction and compute the "reputation" of products, services, or providers, which can assist other users in decision-making in the future. With the rapid growth of online e-Learning content providing services, a suitable reputation system for more credible e-Learning content delivery has become important and is essential if educational content providers are to remain competitive. Most existing reputation systems focus on generating ratings only for user reputation; they fail to consider the reputations of products or services(item reputation). However, it is essential for B2C e-Learning services to have a reliable reputation rating mechanism for items since they offer guidance for decision-making by presenting the ranks or ratings of e-Learning content items. To overcome this problem, we propose a novel collaborative filtering based reputation rating method. Collaborative filtering, one of the most successful recommendation methods, can be used to improve a reputation system. In this method, dual information sources are formed with groups of co-oriented users and expert users and to adapt it to the reputation rating mechanism. We have evaluated its performance experimentally by comparing various reputation systems.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.8
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• pp.315-320
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• 2015

The purposed of this research is to develop multimedia content usage analysis service platform. In the proposed platform, the content gazing behaviors of the users are monitored and profiled in real-time and a set of quantifiable metrics is provided. These metrics are used to determine the closeness of the users' behavior from the intent set by the provider. Based on the evaluation, it is possible to assess the effectiveness of the contents themselves as well. The content usage assessment is accomplished by utilizing the intention flow and the intent weight, which are embedded into the content by the content provider. Proposed methodology can be effectively applied and used in various application domains such as in education and in commercial advertisements.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.117-123
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• 2013

The development of the latest emotional recognition and multimedia technology has changed the traditional broadcasting system. The previous broadcasting system, which was operated by the terrestrial broadcasters, is now transformed to the viewer-centered and bidirectional broadcasting through the convergence of internet, mobile and smart TV. In this paper, smart-phone application for estimating human emotion(sadness, anger, depression) has been developed and emerged with smart TV, thereby we can present broadcasting service for enhancing the sense of common humanity among people of same group. If there is friend in the depression, we can bring comfort to him by inviting one for TV program what I watch and having a honest talk with facial avatar or emoticon. The proposed emotional broadcasting service inter-working with smart-phone application can give feeling of belonging and happiness to the people suffering from the blues, and it can prevent him from attempting suicide. In addition, smart-phone based emotional broadcasting service can be expended to program recommendation service customized to user's emotion, emotional LED lighting service to maximize the sense of reality and home shopping service taking advantage of the mood of customer.

• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1413-1422
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• 2010

With the emergence of digital TV broadcasting, various channels are provided to a TV audience. But it is getting hard for the audience to find his or her preferred TV programs due to the huge number of TV channels. In order to mitigate the difficulty, TV broadcasting companies provide an electronic program guide (EPG), which is a digital guide to scheduled broadcast TV programs. However, it results in the information overload problem and the time-consuming problem since the number of TV channels and programs is gradually on the increase. In this paper, we design and develop a channel filtering system, which recommends a small number of channels by filtering TV channels based on the watching pattern of the TV audience. The channel filtering system does not require the replacement or upgrade of existing TV or set-top box. In addition, it increases usability by skipping the channels that broadcast the audience's non-preferred TV programs while the TV audience presses the channel up/down button.

• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.181-191
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• 2016

We analyzed the search word of the media content in the IPTV service, and as a result we found that an important factor is general meta information as well as content(material, plot, etc.) and emotion information in the media content selection criteria of customers. Therefore, in this research, in order to efficiently provide various media contents of IPTV to users, we designed the emotion classification system for utilizing the emotion information of the media content. Next, we proposed 'personalized media contents curation system based on emotion information' for organizing the media contents, through the various processing steps. Finally, to demonstrate the effectiveness of this system, we conducted a user satisfaction survey(72.0 points). In addition, the results of comparing the results based on popularity and the results of the proposed system showed that the ratio leading to the actual users' viewing behavior was 10 times higher.