Incorporating Social Relationship discovered from User's Behavior into Collaborative Filtering (사용자 행동 기반의 사회적 관계를 결합한 사용자 협업적 여과 방법)
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- 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.
Purpose: The purpose of this study is to evaluate anal sphincter preservation rates, survival rates, and prognostic factors in patients with rectal cancer treated with preoperative chemoradiotherapy. Materials and Methods: One hundred fifty patients with pathologic confirmed rectal cancer and treated by preoperative chemoradiotherapy between January 1999 and June 2007. Of the 150 patients, the 82 who completed the scheduled chemoradiotherapy, received definitive surgery at our hospital, and did not have distant metastasis upon initial diagnosis were enrolled in this study. The radiation dose delivered to the whole pelvis ranged from 41.4 to 46.0 Gy (median 44.0 Gy) using daily fractions of
Go bears significant meanings in terms of cultural and entertaining functions in Asia Eastern such as China and Japan. Beyond the mere entertaining level, it produces philosophical and mythic discourse as well. As a part of effort to seek an identity of Korean traditional garden culture, this study traced back to find meanings of rock-go-board and taste for the arts which ancestors pursued in playing Go game, through analysis and interpretation of correlation among origin of place name, nearby scenery, carved letters and vicinal handed-down place name. At the same time, their position, shape and location types were interpreted through comprehensive research and analysis of stone-go-boards including rock-go-board. Particularly, it focused on the rock names related to Sundoism(仙道) Ideal world, fixed due to a connection between traces of Sundoism and places in a folk etymology. Series of this work is to highlight features of the immortal sceneries, one of traditional landscaping ideals, by understanding place identity and scenic features of where the rock-go-boards are carved. These works are expected to become foundation for promotion and preservation of the traditional landscaping remains. The contents of this study could be summarized as follows; First, round stone and square board for round sky and angled land, black and white color for harmony of yin and yang and 361paths for rotating sky are symbols projecting order of universe. Sayings of Gyuljungjirak(橘中之樂), Sangsansaho(商山四皓), Nangagosa(爛柯故事) formed based on the idea of eternity stand for union of sky and sun. It indicates Go game which matches life and nature spatiotemporally and elegant taste for arts pursuing beauty and leisure. Second, the stone-go-boards found through this research, are 18 in total. 3 of those(16.1%), Gangjin Weolnamsaji, Yangsan Sohanjeong and Banryongdae ones were classified into movable Seokguk and 15(83.9%) including Banghakdong were turned out to be non-movable rock-go-boards carved on natural rocks. Third, upon the result of materializing location types of rock-go-boards, 15 are mountain stream type(83.9%) and 3 are rock peak type(16.1%). Among those, the one at Sobaeksam Sinseonbong is located at the highest place(1,389m). Considering the fact that all of 15 rock-go-boards were found at mountainous areas lower than 500m, it is recognizable that where the Go-boards are the parts of the living space, not far from secular world. Fourth, there are 7 Sunjang(巡將) Go with 17 Hwajeoms(花點), which is a traditional Go board type, but their existences, numbers and shapes of Hwajeom appear variously. Based on the fact, it is recognizable that culture of making go-board had been handed down for an extended period of time. Among the studied rock-goboards, the biggest one was Muju Sasunam[