• MISULJARYO - National Museum of Korea Art Journal
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• v.100
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• pp.140-170
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• 2021

This paper investigates the Wooden Seated Tri-kaya Buddha Images(三身佛像) of Vairocana, Rushana, and Sakyamuni enshrined in Daeungjeon Hall of Hwaeomsa temple(華嚴寺) in Gurae, South Cheolla Province. They were produced in 1634 CE and placed in 1635 CE, about forty years after original images made in the Goryeo period were destroyed by the Japanese army during the war. The reconstruction of Hwaeomsa was conducted by Gakseong, one of the leading monks of Joseon Dynasty in the 17th century, who also conducted the reconstructions of many Buddhist temples after the war. In 2015, a prayer text (dated 1635) concerning the production of Hwaeomsa Tri-kaya Buddha images was found in the repository within Sakyamuni Buddha. It lists the names of participants, including royal family members (i.e., prince Yi Guang, the eighth son of King Seon-jo), and their relatives (i.e., Sin Ik-seong, son-in-law of King Seonjo), court ladies, monk-sculptors, and large numbers of monks and laymen Buddhists. A prayer text (dated 1634) listing the names of monk-sculptors written on the wooden panel inside the pedestal of Rushana Buddha was also found. A recent investigation into the repository within Rushana Buddha in 2020 CE has revealed a prayer text listing participants producing these images, similar to the former one from Sakyamuni Buddha, together with sacred relics of hoo-ryeong-tong copper bottle and a large quantity of Sutra books. These new materials opened a way to understand Hwaeomsa Trikaya images, including who made them and when they were made. The two above-mentioned prayer texts from the repository of Sakyamuni and Rushana Buddha statues, and the wooden panel inside the pedestal of Rushan Buddha tell us that eighteen monk-sculptors, including Eungwon, Cheongheon and Ingyun, who were well-known monk artisans of the 17th century, took part in the construction of these images. As a matter of fact, Cheongheon belonged to a different workshop from Eungwon and Ingyun, who were most likely teacher and disciple or senior and junior colleagues, which means that the production of Hwaeomsa Tri-kaya Buddha images was a collaboration between sculptors from two workshops. Eungwon and Ingyun seem to have belonged to the same community studying under the great Buddhist priest Seonsu, the teacher of Monk Gakseong who was in charge of the reconstruction of Haweonsa temple. Hwaeomsa Tri-kaya Buddha images show a big head, a squarish face with plump cheeks, narrow and drooping shoulders, and a short waist, which depict significant differences in body proportion to those of other Buddha statues of the first half of 17th century, which typically have wide shoulders and long waists. The body proportion shown in the Hwaeomsa images could be linked with images of late Goryeo and early Joseon period. Rushana Buddha, raising his two arms in a preaching hand gesture and wearing a crown and bracelets, shows unique iconography of the Bodhisattva form. This iconography of Rushana Buddha had appeared in a few Sutra paintings of Northern Song and Late Goryeo period of 13th and 14th century. BodhaSri-mudra of Vairocana Buddha, unlike the general type of BodhaSri-mudra that shows the right hand holding the left index finger, places his right hand upon the left hand in a fist. It is similar to that of Vairocana images of Northern and Southern Song, whose left hand is placed on the top of right hand in a fist. This type of mudra was most likely introduced during the Goryeo period. The dried lacquer Seated Vairocana image of Bulheosa Temple in Naju is datable to late Goryeo period, and exhibits similar forms of the mudra. Hwaeomsa Tri-kaya Buddha images also show new iconographic aspects, as well as traditional stylistic and iconographic features. The earth-touching (bhumisparsa) mudra of Sakymuni Buddha, putting his left thumb close to the middle finger, as if to make a preaching mudra, can be regarded as a new aspect that was influenced by the Sutra illustrations of the Ming dynasty, which were imported by the royal court of Joseon dynasty and most likely had an impact on Joseon Buddhist art from the 15th and 16th centuries. Stylistic and iconographical features of Hwaeomsa Tri-kaya Buddha images indicate that the traditional aspects of Goryeo period and new iconography of Joseon period are rendered together, side by side, in these sculptures. The coexistence of old and new aspects in one set of images could indicate that monk sculptors tried to find a new way to produce Hwaeomsa images based on the old traditional style of Goryeo period when the original Tri-kaya Buddha images were made, although some new iconography popular in Joseon period was also employed in the images. It is also probable that monk sculptors of Hwaeomsa Tri-kaya Buddha images intended to reconstruct these images following the original images of Goryeo period, which was recollected by surviving monks at Hwaeomsa, who had witnessed the original Tri-kaya Buddha images.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.