• Journal of the Daesoon Academy of Sciences
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• v.43
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• pp.67-102
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• 2022

The purpose of this study is to analyze the human rights found in the North Korean Constitution and their core problem by focusing on elements of human rights suggested by Daesoon Jinrihoe's doctrine of Haewon-sangsaeng (解冤相生 the Resolution of Grievances for Mutual Beneficence). Haewon-sangsaeng is seemingly the only natural law that could resolve human resentment lingering from the Mutual Contention of the Former World while leading humans work for the betterment of one another. Haewon-sangsaeng, as a natural law, includes the right to life, the right to autonomous decision-making, and duty to act according to human dignity (physical freedom, the freedom of conscience, freedom of religion, freedom of speech, freedom of press, etc.), the right to equal treatment in one's social environment, and the right to ensure the highest level of health through treatment. The North Korean Constitution does not have a character as an institutional device to guarantee natural human rights, the fundamental principle of the Constitution, and stipulates the right of revolutionary warriors to defend dictators and dictatorships. The right to life is specified so that an individual's life belongs to the life of the group according to their socio-political theory of life. Rights to freedom are stipulated to prioritize group interests over individual interests in accordance with the principle of collectivism. The right to equality and the right to health justify discrimination through class discrimination. The right to life provided to North Koreans is not guaranteed due to the death penalty system found within the North Korean Criminal Code and the Criminal Code Supplementary Provisions. The North Korean regime deprives North Koreans of their right to die with dignity through public executions. The North Korean regime places due process under the direction of the Korea Worker's Party, recognizes religion as superstition or opium, and the Korea Worker's Party acknowledge the freedoms of bodily autonomy, religion, media, or press. North Koreans are classified according to their status, and their rights to equality are not guaranteed because they are forced to live a pre-modern lifestyle according to the patriarchal order. In addition, health rights are not guaranteed due biased availability selection and accessibility in the medical field as well as the frequent shortages of free treatments.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.