• Cartoon and Animation Studies
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• s.42
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• pp.293-313
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• 2016

The target stimulating human's sensitivity can include several things; the first is human like us including beautiful women and cute babies. The next ranking is the dog animal that established itself as a human's companion. It is the 3B law(beauty, baby, beast - much used in the ad or election due to the positive image) that is widely used in the advertising. This relationship is being expressed in the art history for a long time. Dogs that have lived a history more than 10 thousand years with humans hunted, protected flock of sheep, and kept the farmer's house and property. They have been human's assistants and companion who entered into the modern urban culture. Like this, dogs have adapted to several situations endlessly such as the nomadic life, farming, country life, and urban life. This paper will explore the close relationship of humans and companion animals through various icons of dogs and pups that appeared through a number of artists' skills. The companion animal means an animal that lives with people, which means the relationship of round-trip rather than the one-way relation those each other gives a help. Therefore, there artist tries to examine the figure of great hunter for survival, highly evaluated figure as the royal dignity, and the mascot-like figure delivering the daily happiness to modern people as presenting joy through a discussion of the 'countenance', a visual signal of the dogs and pups. They have been influenced by screen and popular media in 20C. Snoopy, a main character of and the movie <101 Dalmatians> made a success on the theater and television when Beagle and Dalmatian were prevalent. These main characters make audience feel happy involuntarily. Like this, the continuous and old friendship of the human and dogs will be confirmed again through the icon of dogs and pups consisting of the communication of artists and readers in the modern shape art, and it is hoped to be a psychological stabilizing effect to modern people living in the intense modern society. Therefore, it is expected this study paper will be reborn as a new text and be expanded as an effective communication in the journey of dogs and human in the future in investigating the communion of dogs and human.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used