• Journal of Hospice and Palliative Care
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• v.7 no.1
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• pp.17-28
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• 2004

Purpose: Even though there have been various efforts for the dying with dignity of terminal patients, no researches focused on the public attitudes. Methods: In February 2004, we sampled 1,055 persons over 20 years of age from the sixteen cities and local districts of Korea through the quota sampling method according to their gender, age, and location. We conducted a telephone survey with a structured questionnaire on the attitudes toward dying with dignity and hospice palliative care. Results: The most important conditions for the dying with dignity on the patients' views were 'removing burdens for other people' (27.8%). Over the half of the samples chose their home as a preference for place of death (54.8%). 82.3% of the respondents agreed to the idea of withdrawing the medically futile life-sustaining treatment. Fifty seven percents of the answered public said that they intended to use the hospice service in case of terminal illness. Eighty percents thought that health care insurance should cover hospice service, and 80.9% gave positive response to the necessity of advance directives. Respondents emphasized 'the financial support for the terminal patients' (29.8%), 'covering hospice service with health insurance' (16.5%), and 'the education and public relation for settlement of desirable dying culture and hospice service' (15.9%) as the roles and responsibilities of the government for the dying with dignity. Conclusion: This study shows that there is a possibility of significant consensus on hospice and palliative care system for the dying with dignity of patients and reduction of the suffering for their families among the general public.

• Korean journal of food and cookery science
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• v.28 no.4
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• pp.413-421
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• 2012

This study was conducted with 418 adults 20 years or older, all of whom lived in Daegu and Gyeongbuk. According to a survey, the number of females was greater than that of males (40.7%) by 59.3%, and that for ages 30 years was the highest. The preference for Korean traditional drinks was relatively high at 51.8%, and the frequency of drinking Korean traditional drinks was 39.0%. The adults answered that they had these beverages on special days such as holidays, ritual days, and birthdays. Among the reasons for drinking a traditional beverage 'good taste' scored the highest with 27.0% of respondents, followed by 'Korean traditional food' with 24.4%. The recognition of Korean traditional drinks was high in the order of Sikhe, Soojunggwa, Cha, and Hwachae. The preference for Sikhe was the highest. The group who agreed that it was important to develop a Korean functional traditional drink was 11.5% higher than that of the negative group, as 13.4% 'agreed a lot' and 41.1% 'agreed'. Consumer awareness toward traditional drink functionality was generally positive, with 3.5 points or higher on average, and awareness of the nutritional supplementation, diabetes control, the recuperative effects of the drinks were also high. Among Korean traditional drinks Sikhe was the highest with regard to intention to purchase. As a result, the popularization of traditional Korean drinks was based on three factors: quality oriented image, popularity oriented image, and product attribute-oriented image. These factors significantly influenced the preference for and purchase of Korean functional traditional drinks.

• Journal of agriculture & life science
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• v.46 no.6
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• pp.97-104
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• 2012

The aim of this study was to investigate the quality properties of beef jerky replaced salt (NaCl) with red pepper paste, soy sauce and soybean paste. The quality properties of beef jerky including water activity ($a_w$), pH, moisture content, protein content, color, shear force, texture profile analysis and sensory evaluations were investigated. The sliced beef samples were marinated at salt (control), soybean paste (T1), soy sauce (T2) and red pepper paste (T3) for 24 h and then dried at $70^{\circ}C$ for 8 h. The $a_w$ and moisture content varied from 0.88 to 0.79 and from 28.87% to 22.98%, respectively. All treatment samples showed higher final $a_w$ and moisture content than the control sample after drying for the 8 h (p<0.05). The protein content of T2 and T3 samples were lower than the control. Also, shear force and hardness value of all treatment samples had lower than the control (p<0.05). However, all treatment samples showed lower saltiness intensity than the control sample. Sensory panelists recorded greater flavor and texture scores to the samples with soy sauce replacement. Therefore, sensory panels found that the T2 samples had better overall acceptability scores than the other beef jerky samples (p<0.05).

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• Journal of Communication Design
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• v.68
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• pp.278-288
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• 2019

The purpose of this study is to analyze the effects of risk and convenience on purchase intention in the IOT market, and I want to analyze the moderating effect of emotional consumption value. In this study, two products were selected from three product groups. There are three major methods of research. First, theoretical considerations. Second, survey analysis. Reliability analysis and factor analysis were performed using descriptive statistics using SPSS. Third, we measured changes of EEG according to in - depth interview and indirect experience. As a result of the hypothesis of this study, it was confirmed that convenience of use of IoT product influences purchase intention. Risk was predicted to have a negative effect on purchase intentions, but not significant in this study. This implies that IoT products tend to be neglected in terms of monetary loss such as cost of purchase, cost of use, and disposal cost when purchasing. In-depth interviews and EEG analysis revealed that there is a desire to purchase and try out the IoT product due to the nature of the product, the novelty of new technology, and the vague idea that it will benefit my life. The aesthetic, symbolic, and pleasure factors, which are sub - elements of emotional consumption value, were found to have a great influence. This is consistent with previous research showing that emotional consumption value has a positive effect on purchase intention. In-depth interviews and EEG analyzes also yielded the same results. This study has revealed that emotional consumption value affects the intention to purchase IoT products. It seems that companies producing IoT products need to concentrate on marketing with more emotional consumption value.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.223-252
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• 2008

In this study, we attempt to propose a choice-based diffusion model with switching cost, which can be used to forecast the dynamic substitution and competition among previous and new products at both individual-level and aggregate level, especially when market data for new products is insufficient. Additionally, we apply the proposed model to the empirical case of substitution and competition among Analog Cable TV that represents previous fixed charged broadcasting service and Digital Cable TV and Internet Protocol TV (IPTV) that are new ones, verify the validities of our proposed model, and finally derive related empirical implications. For empirical application, we obtained data from survey conducted as follows. Survey was administered by Dongseo Research to 1,000 adults aging from 20 to 60 living in Seoul, Korea, in May of 2007, under the title of 'Demand analysis of next generation fixed interactive broadcasting services'. Conjoint survey modified as follows, was used. First, as the traditional approach in conjoint analysis, we extracted 16 hypothetical alternative cards from the orthogonal design using important attributes and levels of next generation interactive broadcasting services which were determined by previous literature review and experts' comments. Again, we divided 16 conjoint cards into 4 groups, and thus composed 4 choice sets with 4 alternatives each. Therefore, each respondent faces 4 different hypothetical choice situations. In addition to this, we added two ways of modification. First, we asked the respondents to include the status-quo broadcasting services they subscribe to, as another alternative in each choice set. As a result, respondents choose the most preferred alternative among 5 alternatives consisting of 1 alternative with current subscription and 4 hypothetical alternatives in 4 choice sets. Modification of traditional conjoint survey in this way enabled us to estimate the factors related to switching cost or switching threshold in addition to the effects of attributes. Also, by using both revealed preference data(1 alternative with current subscription) and stated preference data (4 hypothetical alternatives), additional advantages in terms of the estimation properties and more conservative and realistic forecast, can be achieved. Second, we asked the respondents to choose the most preferred alternative while considering their expected adoption timing or switching timing. Respondents are asked to report their expected adoption or switching timing among 14 half-year points after the introduction of next generation broadcasting services. As a result, for each respondent, 14 observations with 5 alternatives for each period, are obtained, which results in panel-type data. Finally, this panel-type data consisting of $4{\ast}14{\ast}1000=56000$observations is used for estimation of the individual-level consumer adoption model. From the results obtained by empirical application, in case of forecasting the demand of new products without considering existence of previous product(s) and(or) switching cost factors, it is found that overestimated speed of diffusion at introductory stage or distorted predictions can be obtained, and as such, validities of our proposed model in which both existence of previous products and switching cost factors are properly considered, are verified. Also, it is found that proposed model can produce flexible patterns of market evolution depending on the degree of the effects of consumer preferences for the attributes of the alternatives on individual-level state transition, rather than following S-shaped curve assumed a priori. Empirically, it is found that in various scenarios with diverse combinations of prices, IPTV is more likely to take advantageous positions over Digital Cable TV in obtaining subscribers. Meanwhile, despite inferiorities in many technological attributes, Analog Cable TV, which is regarded as previous product in our analysis, is likely to be substituted by new services gradually rather than abruptly thanks to the advantage in low service charge and existence of high switching cost in fixed charged broadcasting service market.

• Journal of Food Hygiene and Safety
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• v.27 no.1
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• pp.42-49
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• 2012

This study was carried out to investigate the toxicity of food Red No.2 in the Sprague-Dawley (SD) female rat for 4 weeks. SD rats were orally administered for 28 days, with dosage of 500, 1,000, 2,000 mg/kg/day. Animals treated with food Red No.2 did not cause any death and show any clinical signs. They did not show any significant changes of body weight, feed uptake and water consumption. There were not significantly different from the control group in urinalysis, hematological, serum biochemical value and histopathological examination. In conclusion, 4 weeks of the repetitive oral medication of food Red No.2 has resulted no alteration of toxicity according to the test materials in the group of female rats with injection of 2,000 mg/kg. Therefore, food Red No.2 was not indicated to have any toxic effect in the SD rats, when it was orally administered below the dosage 2,000 mg/kg/day for 4 weeks.