• Journal of Sasang Constitutional Medicine
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• v.8 no.1
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• pp.377-393
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• 1996

Lumbago is the common disease in the human, many people have been sicked the Lumbago. As the traditional methods of Lumbago treatment, acupuncture, moxibustion, herb medicines have been applied to a patient, nowadays, new methods have been applied physical remedy, Yakchim (Korean herb acupuncture) remedy, Chuna remedy to. This report was collected 73 Lumbago patients by name, sex, age, motive, symptoms, X-ray, CT, MRI, lRCT, Sasang constitution type, Sasang herb medicine, Yakchim, Chuna, period of remedy, satisfaction of remedy, at the "WooRee Korean Medical Clinic" during 21 months from Sep. 14, 1994 to May 25, 1996. And this report was studied about the distribution of the Sasang constitution type, the Sasang herb medicine, the effect, the period. The results were as follows: 1. Lumbago patients were distributed like that; Taeum-ln (太陰人) 47 (66.3 %), Soyang-In 16 (21.9 %), Soum-In (13.7 %), Taeyang-In (太陽人) 0. This was different from distribution of Donguisuseibowon (東醫壽世保元), Taeum-In (太陰人) 50%, Soyang-In (少陽人) 30 %, Soum-In (少陰人) 20 %, Taeyang-In (太陽人) little, this report shows that the number of Taeum-In (太陰人) is more than that of Donguisuseibowon and the number of Soum-In is less than that of Donguisuseibowon. 2. The average satisfaction of remedy was 60.3 %, Taeum-In's satisfaction was 66.0 %, Soum-In's satisfaction was 56.3 %, Soyang-In's satisfaction was 60.0 %. 3. The effective herb medicines were as follows, Soyang-In used the Hyong Bang Ji Hwang Tang (荊防地黃湯), Yuk Mi Ji Hwang Tang (六味地黃樓), Soum-In used the Sib Yi Mi Goan Jung Tang (十二味寬中湯), Taeum-In used the Chung Sim Yon Ja Tang (淸心蓮子陽), Chung Pae Sa Gan Tang (淸師爾肝湯), Yeol Da Han So Tang (熱多寒少湯). 4. The period of remedy was about 6 weeks. The period of remedy of each types was as follows, Taeum-In was about 5.7 weeks, Soum-In was about 6.8 weeks, Soyang-In was about 4.2 weeks. 5. The method of Lumbago remedy is divided three types, sprain Lumbago, Pyobyong (表病 : outside Syndromes) Libyong (裡病 : inside symdromes). Soum-In's methods are Pyobyong's ascending the Yang (陽), adding the Gi (氣) [升陽益氣], and Libyong's descending the inside Yim (裡陰) [裡陰降氣], Soyang-In's methods are Pyobyongs's decending the outside-Yim [表陰降氣], and Libyong's ascending the cool Yang (濟陽) [淸陽上升]. Taeum-In's methods are Pyobyong's ascending the Lung's Yang (肺陽升氣), and Libyong's colding the dried hot liver (淸肝燥熱). Taeyang's methods are strong the liver and making Yim. (補r肝生陰) 6. There are two methods for using the YakChim (Korean herb-acupuncture) by Sasang constitution medicine, one is to select the Yakchim, the other is to choice the point for appling the Yakchim. The first, to select the Yakchim, the other is follows; Soum-In can select the bee Venom, Soyang-In can select the H.O. (Hong Whoa 紅花), Taeum-In can select the I (Hodo 胡挑), V, O.K. (Ungdarn, 薦膽), Uwhang 牛黃, Sa-Hyang 麝香, etc., Palgang Yakchim (eight principles Korean herb-acupuncture (八剛藥鐵)) could made by abstracted Sasang herb medicine. The second, to choice the points for applying the Yakchim are used in the TaeGiuk Acupuncture method (太梗針法), Sacho (四焦, four warmer) by Sasang constritutional physiology and pathology.

• 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.