• Journal of the Daesoon Academy of Sciences
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• v.37
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• pp.191-227
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• 2021

In this study, values of truth and good are expressed in the form of beauty, and truth and good are analyzed from an aesthetic point of view. This enables an assessment of how truth is expressed and presented as an "aesthetic" in Daesoon Thought. Therefore, an approach to faith in Daesoon Jinrihoe (大巡眞理會) can be presented via traditional aesthetics or theological aesthetics that reflect on sense experience, feelings, and beauty. The concept of beauty in Daesoon Thought which focuses on The Canonical Scripture appears in keywords used in Daesoon Thought such as divine nature (神性), the pattern of Dao (道理), the singularly-focused mind (一心), and relationships (關係). Therein, one can find sublimation, symmetry, moderation, and harmony. The aesthetic features of Daesoon Thought, when considered as an aesthetic system can formulate thinking regarding the aesthetics of 'Reordering Works of Heaven and Earth' (天地公事), the aesthetics of Mutual Beneficence (相生), and the aesthetics of healing. The Reordering Works of Heaven and Earth contain a record of the Supreme God visiting the world as a human being. The realization that the human figure, Kang Jeungsan (1871-1909), is the Supreme God, Sangje (上帝), is the shocking aesthetic motif and theological starting point of the Reordering Works of Heaven and Earth. Mutual Beneficence can be seen aesthetically as indicating the sociality of mutual relations, and there is an aesthetic structure of Mutual Beneficence in the harmony and unification of those relations. Healing can be said to contain the sacred sublimation of Sangje, and moderation is a form of beauty that makes humans move toward Quieting the mind and Quieting the body (安心·安身), the Dharma of Presiding over Cures (醫統), and the ultimate value of healing, which is the end point of the Cultivation (修道) wherein one realizes that the ideals of humankind and the aesthetics of healing bestow the spiritual pleasures of a beautiful and valuable life. The aesthetic characteristics of Daesoon Thought demonstrate an aesthetic attitude that leads to healing through Sangje's Holy Works and the practice of Mutual Beneficence (相生) which were performed when He stayed with us to vastly save all beings throughout the Three Realms that teetered on the brink of extinction. It is not uncommon to see a beautiful woman and remark she is like a goddess (女神) or female immortal (仙女). Likewise, beautiful music is often praised as "the sound of heaven." That which fills us with joy is spoken of as "divine beings (神明)" of God. God is a symbol of beauty, and the world of God can be said to be the archetype of beauty. Experience of beauty guides our souls to God. The aesthetic experience of Daesoon Thought is a religious experience that culminates in emotional, intellectual, and spiritual joy, and it is an aesthetic experience that recognizes transcendent beauty.

• Journal of the Daesoon Academy of Sciences
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• v.31
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• pp.165-197
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• 2018

The purpose of this article is to investigate communal action in the Ox Seeking Pictures of Daesoon Thought as an expression of future prospects. The Ox Seeking Pictures in Daesoon Thought seeks out renewal of thought, renewal of life, and true living. Here, the Ox Seeking Pictures symbolize a world in which good fortune comes true according to faith in Gucheon Sangje. The correlation between searching for the ox and the supporting teachings of the Reordering Works of Heaven and Earth in Daesoon shows the transformation of Daesoon prospects for achieving the renewal of thought. The correlation between Deep Contemplation Leading to Awakening and Finding and Following Heavenly Teachings shows the transformation of Daesoon reason into a practice implemented in daily life. The correlation between a human being's awareness and the heavenly paradise of the Later World shows transformation into true living based on everyday practice and the practical transformation of one's livelihood. In this investigation, we can say that the Ox Seeking Pictures of Daesoon symbolizes the realization of human dignity and respect for lives. No life should be destroyed or violated by another. Heaven, Earth, and Humanity can be changed and born anew. The visions of the realization of the heavenly paradise of the Later World show that this paradise in the world results from Daesoon principles. This provides a unique insight when compared to the bodhisattva ideal conveyed through the Ox Seeking Pictures of Mahayana Buddhism. Daesoon's Ox Seeking Pictures consist of a three-way interlocking of renewal of thought, implementation in life, and the practical transformation of one's livelihood. The communal spirituality based in Daesoon Truth connects and mediates among people and appears in three aspects. Firstly, it is thought to be a vision of the renewal of thought through the 'Virtuous Concordance of Yin and Yang.' Secondly, it is thought to be the vision of a new life based upon the spirit of Mutual Beneficence. Thirdly, it is thought to be a vision of true living through the realization of human dignity. Because of the appearance of the Ox Seeking Pictures of Daesoon Thought, this narrative picture shows the oxherd as searching for an ox which is the symbol of Daesoon Truth and Dao. Even though he catches the ox, he is still holds the rope to tie the ox to himself. He makes an effort to keep the ox steady. Finally, the oxherd's enlightenment becomes the source of responsibility to help unenlightened people in their struggles. In conclusion, it is necessary to interpret these paintings as the start of the Later World.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• 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.