A Study on the Elements of Interior Design in Victorian Style (빅토리안 스타일 주택 실내 디자인에 관한 연구)
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- Archives of design research
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- v.18 no.4 s.62
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- pp.25-34
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- 2005
The purpose of the present study is to investigate the characteristics of the current Victorian-style interior by reviewing the basic Victorian-style house in the past. this research was analyzed various prior studies and literatures, and found the following results: First, the Victorian-style house and interior space showed various historical trends and adopted every style from Gothic to rococo, and sometimes more than one style influenced a single place. Its formality was applied depending on the function and standard of each room. Second, the interior had many decorative things with free, irregular or other patterns, influenced by Romanticism and Naturalism. The several environmental factors such as air pollution and hygienic matter were also related with its trend. the dramatic changes in the kitchen and sanitary facilities were appeared based on the technical development, and affluent design styles were also used. All these reflected the characteristics of the Victorian age. In conclusion, the characteristics of Victorian-style were influenced by many factors including: (a) the trend of Romanticism and Naturalism, (b) consideration of family convenience based on the technical development, (c) the Socio-Environmental factors like air pollution and the social norm, and (d) reflection of the individual value in accordance with frequent contacts with foreign cultures. In this respect, it is necessary to reevaluate the Victorian-style after paying due regard to such factors.
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