• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.203-214
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• 2023

The Korea Customs Service is efficiently handling business with an electronic customs system that can effectively handle one-stop business. This is the case and a more effective method is needed. Import and export require HS Code (Harmonized System Code) for classification and tax rate application for all goods, and item classification that classifies the HS Code is a highly difficult task that requires specialized knowledge and experience and is an important part of customs clearance procedures. Therefore, this study uses various types of data information such as product name, product description, and product image in the item classification request form to learn and develop a deep learning model to reflect information well based on Multimodal representation learning. It is expected to reduce the burden of customs duties by classifying and recommending HS Codes and help with customs procedures by promptly classifying items.

• Information Systems Review
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• v.23 no.3
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• pp.77-96
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• 2021

This research investigates the pivotal role of AI-enabled technologies in vitalizing the local economy. Collaborating with a leading search engine company, we examine the direct and indirect of an AI-based location search service on the success of sampled 7,035 local restaurants in Gangnam area in Seoul. We find that increased use of AI-enabled search and recommendation services significantly improved the selections of previously less-discovered or less-popular restaurants by users, and it also enhanced the stores' overall conversion rates. The main research findings have contributions to extant literature in theorizing the value of AI applications in local economy and have managerial implications for search businesses and local stores by recommending strategic use of AI applications in their businesses that are effective in highly competitive markets.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.57-64
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• 2015

Activity recognition technology is gaining attention because it can provide useful information follow user's situation. In research of activity recognition before smartphone's dissemination, we had to infer user's activity by using independent sensor. But now, with development of IT industry, we can infer user's activity by using inner sensor of smartphone. So, more animated research of activity recognition is being implemented now. By applying activity recognition system, we can develop service like recommending application according to user's preference or providing information of route. Some previous activity recognition systems have a defect using up too much energy, because they use GPS sensor. On the other hand, activity recognition system which Google released recently (Google Activity Recognition) needs only a few power because it use 'Network Provider' instead of GPS. Thus it is suitable to smartphone application system. But through a result from testing performance of Google Activity Recognition, we found that is difficult to getting user's exact activity because of unnecessary activity element and some wrong recognition. So, in this paper, we describe problems of Google Activity Recognition and propose AGAR(Advanced Google Activity Recognition) applied method to improve accuracy level because we need more exact activity recognition for new service based on activity recognition. Also to appraise value of AGAR, we compare performance of other activity recognition systems and ours and explain an applied possibility of AGAR by developing exemplary program.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.1-23
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• 2011

The contextual subjective well-being (SWB) of context-aware system users can be very helpful in recommending relevant mental health services, especially for those who struggle with mental illness due to a metabolic syndrome or melancholia. Self-surveying measuring or auto-sensing methods have been suggested to monitor users' SWB. However, self-surveying measuring method is not inappropriate for a context-aware service due to requesting personal data in a manual and hence obtrusive manner. Moreover, auto-sensing methods still suffer from accuracy problem to be applied in mental health services. Hence, the purpose of this paper is to propose a contextual SWB estimation method to estimate the user's mental health in unobtrusive and accurate manners. This method is timely in that it acquires context data from the user's literal responses, which expose their temporal feeling. In particular, we developed a measuring method based on exposed feeling verbs and degree adverbs in chat and other text-based communications which show anger or negative feelings. Based on the proposed contextual SWB degree estimation method, we developed an idea of well-being life care recommendation. From the experiment with actual drivers, we demonstrated that the proposed method accurately estimate the user's degree of negative feelings even though it does not require a self-survey.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.127-138
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• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.

• Journal of agricultural medicine and community health
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• v.27 no.2
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• pp.87-105
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• 2002

In order to find out the knowledge, attitude, and experience of community health practitioners(CHP) on complementary therapy, 393 community health practitioners who provide primary health care service in Busan, Kyeongnam, and Daegu, Gyeongbuk regions were interviewed or surveyed by mail from February 1st to March 31st, 2002. In terms of interest of CHPs toward 11 different hinds of complementary therapy, the rate of interest for soojichim was the highest with 75.3%. Aroma therapy had the interest of 71.0% of the CHPs, oriental medicine had 67.4%, and massage had 67.4%. The interest for shiatsu was 64.6%, while homeopath had the lowest rate of interest of 18.1%. In terms of reliance on the treatment results, oriental medicine scored the highest with 92.6%, and soojichim, massage, and shiatsu followed with 85.5%, 83.7%, and 81.7% respectively. Homeopath had the lowest reliance of 18.1%. The 65.1% of the CHPs had the experience of recommending oriental medicine to patients. 50.4% indicated that they had recommended soojichim, and 44.8% had recommended massage before. Shiatsu and aromatherapy followed with 34.4% and Homeopath had the lowest rate of 2.80%. When CHPs were asked if they had received any training in complementary therapy, 33.1% indicated that they had studied soojichim and 13.2%stated that they had learned oriental medicine. Aromatherapy, massage, and shiatsu followed with 11.2%, 8.4%, and 5.6% respectively On the other hand, none of the CHPs had received training in homeopath. In terms of using complementary therapy during the past 5 years, 23.9% had been treated with oriental medicine, and 18.896 had received soojichim. 5.9% had received aromatherapy, 5.3% had used massage, and 5.1% had experience with shiatsu. None of the practitioners had used homeopath during the past 5 years. Significantly many number of practitioners indicated that they had excellent treatment results with all hinds of complementary therapy, and there were rare cares of side effects. When they were asked if they wanted complementary therapy to become part of the curriculum during re-training or training for public service personnels, 78100 wanted soojichim, 69.2% wanted oriental medicine, and 67.9% wanted aroma therapy. 63.9% wanted shiatsu to be included, and 63.1% wanted massage. When CHPs were asked if they wanted to use complementary therapy during primary health care, 63.6% wanted to use soojichim, 52.9% wanted massage, and 51.9% wanted to use aroma therapy. Oriental medicine also showed a high rate of 50.1%. On the other hand, only a small percentage wanted to use chiropractic or homeopath with 17.0% and 12,2% respectively. Among the CHPs, there were some who had administered complementary therapy during the past 5 years. 84% had administered soojichim, 4.6% had administered oriental medicine, and 2.5% had administered massage 2.5% of the CHPs answered that they had administered aromatherapy. However, none of them had administered apitherapy or homeopath. Most of patients showed positive responses, and the rate of side effect was very low. As shown in the above results, although CHPs have a high rate of interest, reliance, and experience in recommending complementary therapy, only a low percentage of them had received any training in complementary therapy. In addition, since there were little side effects when they received or administered complementary therapy, they hoped complementary therapy, which can be beneficial to health, to be introduced to the curriculum. Therefore, in order to provide community members with complementary therapy and the correct information regarding the selection of complementary therapy that could be beneficial to health, a policy of continuous interest and support is needed so that CHPs can he provided with a systemic and rational curriculum of complementary therapy.

• Culinary science and hospitality research
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• v.17 no.3
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• pp.20-32
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• 2011

The purpose of this study is to analyze customers' behavior in using word-of-mouth(WOM) information about Korean local food restaurants. The questionnaire developed for this study was distributed to 500 customers living in Jeonbuk area and a total of 455 copies (91.0%) were used for analysis. The statistical analysis was conducted using SPSS Win(12.0). The results were summarized as follows. The recommendation of people experienced'($M=3.57{\pm}1.24$) and 'word-of-mouth through people around'($M=3.52{\pm}1.20$) were major word-of-mouth information sources of Korean local foods; 'taste of food'($M=4.16{\pm}1.15$) and 'service quality'($M=3.79{\pm}1.11$) were important attributes in word-of-mouth information. In addition, to the question about the reasons for recommending the restaurant to the people around, the most people replied that 'flavor, nutrition and quality of local foods can be kept only in the specific location' ($3.53{\pm}1.08$), followed by 'to keep the memory of the visit to the areas in mind through local foods'($3.51{\pm}1.03$). These results showed that people usually recommend a restaurant based on the quality of the food itself or local characteristics. As a result, it is deemed that word-of-mouth effect is an important factor for the spread of Korean local foods.

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.15-28
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• 2016

This paper proposes a system recommending spatial information what user wants with collecting and analyzing tweets around the user's location by using the GPS information acquired in mobile. This system has built an emotion dictionary and then derive the recommendation score of morphological analyzed tweets to provide not just simple information but recommendation through the emotion analysis information. The system also calculates distance between the recommended tweets and user's latitude-longitude coordinates and the results showed the close order. This paper evaluates the result of the emotion analysis in a total of 10 areas with two keyword 'Restaurants' and 'Performance.' In the result, the number of tweets containing the words positive or negative are 122 of the total 210. In addition, 65 tweets classified as positive or negative by analyzing emotions after a morphological analysis and only 46 tweets contained the meaning of the positive or negative actually. This result shows the system detected tweets containing the emotional element with recall of 38% and performed emotion analysis with precision of 71%.

• Journal of Information Technology Applications and Management
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• v.26 no.4
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• pp.51-69
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• 2019

One of the most considerate phenomena of the era of the Fourth Industrial Revolution is the use of digital devices. Digitalization is rapidly advancing through all areas of industry and life. Customer journey with digitalization is looking totally different from previous customer journey. The research targets were users of fashion, automobiles, cosmetics and online shopping malls. We analyzed 300 people for each valid questionnaire. The results of the study are as follows. First, it has been proven that digital experience affects positive (+) impact on purchasing intention and positive (+) impact on recommending intention and negative impact (-) on switching intent and subsequently affects positive impact (+) to purchase and incase of switching intent, negative impact (-) to purchase. Unlike traditional methods such as SPC(Service Profit Chain), the Digital experience to Purchase process Chain (DPC) has been identified to be suitable in the digital age. Second, the digital satisfied group (5 score-very satisfaction) has shown same result as above. However the digital neutral group (even though 4 score- satisfaction in five-point scale), specially in a highly competitive industry, has different from the satisfied group and 3 score-normal is same as dissatisfied group. It means that this group is that If there is a high level of attractiveness of substitute goods, there is a high possibility of switching them. It has supported Jones and Sasser [1995] that there have been two types of loyalty of true long-term loyalty and what we call false loyalty in the highly competitive industry zone which is commoditization or low differentiation, many substitutes, low cost of switching. Identifying true loyalty and false loyalty is crucial to establishing a customer experience strategy. it is necessary to actively utilize long-term digital experiences strategy to increase the total satisfaction of digital experience through all of customer purchasing journey in order to enhance the digital customer experience. It is difficult to see the effect as a one-time event. It should be scaled over the entire customer purchase process over a long period of time, which can positively affect purchase intention, recommendation intention, and conversion intention. This is also why it is difficult for second-runners to overtake first-runners in a short period.