• Cartoon and Animation Studies
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• s.43
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• pp.285-320
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• 2016

Cartoons and Animation are convergent arts created with a composite application of language arts described in the form of literary texts and sounds, plastic arts visualized in the form of artistic paintings, and film arts produced in the form of moving pictures. An academic university major in cartoons and animation studies established in late 20th century however, did not satisfactorily meet the needs in academic research and development and the free expression of artistic creation was limited. In order to systematize the major in cartoons and animation studies, an convergent approach to establish and clarify following are in demand : the terms and definitions, the historical developments, the research areas and methods, the major education and related jobs and start-ups. New culture and arts industries including cartoons, animation, moving images, and games contents are not yet listed in the industries listing service jointly provided online by the portal site Naver.com and Hyung-Seol publishing company. Above all, cartoons and animation are inseparably related to each other that even if one uses the term separately and independently, the meaning may not be complete. So a new combined term "Animatoon" can be established for the major in cartoons and animation studies and also used for its degree with concentrations of cartoons, animation, moving images, games, and etc. In the Introduction, a new combined term Animatoon is defined and explained the use of this term as the name of the major and degree in cartoons and animation studies. In the body, first, the Historical Developments classified Animatoon in the ancient times, the medieval times, and the modern times and they are analyzed with the help of esthetics and arts using examples of mural frescos, animal painting, religion cartoons, caricatures, cartoons, satire cartoons, comics, animation, 2 or 3 dimensional webtoons, and K-toons. Second, the Research Areas of Animatoon reviewed the theories, genres, artworks, and artists and the Research Methods of Animatoon presented the curriculum that integrated the courses in humanities, science technologies, culture and arts, and etc. Third, the Major Education considered Animatoon education in children, young adults, students of the major and the Related Jobs and Start-Ups explored various jobs relating to personal creation of artwork and collective production of business-oriented artwork. In the Conclusion, the current challenges of Animatoon considered personalization of the artists, specialization of the contents, diversification of the types, and liberalization of the art creation. And the direction of improvement advocated Animatoon to be an academic field of study, to be an art, to be a culture, and to be an industry. The importance of cartoons and animation along with videos and games rose in the 21st century. In order for cartoons and animation to take a leading role, make efforts in studying Animatoon academically and also in developing Animatoon as good contents in the cultural industries.

• 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.