The Computerized 3-D Clothing Simulation for the Evaluation of Men's Working Pants (남성용 작업복 팬츠 3차원 가상착의 시뮬레이션 평가)
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- Journal of the Korean Society of Costume
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- v.63 no.8
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- pp.27-42
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- 2013
The study was aimed to develop men's working pants patterns through the computerized 3-D virtual clothing simulation system and to verify the effects of the 3-D simulated outfit by comparing it to the images of the actual outfits. The average body measurements of South Korean men aged between 30 and 39 used for the simulation in order to generate a 3-D virtual model and to realize outfits of men's working pants for the workers in the heavy industry in South Korea. And also the preliminary questionnaire survey results on certain aspects of the working pants such as type, detailed design preference and discomforting parts were taken into consideration. Both the simulated and real images of the developed working pants were compared in terms of the ease amount according to parts of the working pants, the position of seam lines, the appearance of darts and pleats, and the effects of the fabric surface according to expertise panels' subjective 5-point scale evaluation. The results throughout the study were (1) the basic working pants item worn by subject workers were the straight one pleated pants. The most discomforting parts of the working pants were in the order of body rise, thigh, hip, waist, pants hems and knee girth. (2) the drafting factors of pants patterns differed by the men's body features, which was related to the allocation of suppression amounts between waist and hip girths into darts and hip curve amounts on the waist line level of the pants. (3) the similarity of the virtually simulated and real images of men's working pants resulted in an average of 4.5 to the ease of appearance, 4.6 to the seam lines, 4.1 to the fabric surface effects in a 5-point scale, which means that the two were highly alike.
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