• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.133-140
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• 2012

In this paper, we propose a music recommendation method considering users' tags by collaborative tagging in a social music site. Since collaborative tagging allows a user to add keywords chosen by himself to web resources, it provides users' preference about the web resources concretely. In particular, emotional tags which represent human's emotion contain users' musical preference more directly than factual tags which represent facts such as musical genre and artists. Therefore, to classify the tags into the emotional tags and the factual tags and to assign weighted values to the emotional tags, a tag ontology called UniTag is developed. After preprocessing the tags, the weighted tags are used to create user profiles, and the music recommendation algorithm is executed based on the profiles. To evaluate the proposed method, a conventional playcount-based recommendation, an unweighted tag-based recommendation, and an weighted tag-based recommendation are executed. Our experimental results show that the weighted tag-based recommendation outperforms other two approaches in terms of precision.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1625-1631
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• 2008

This paper is going to research about RFID real time position finder technology and the offline shopping mall's client shop list managed by the RF fused Tag USB memory to analyze out the output of the data for providing real time interactive customer intelligence commodity system.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.133-141
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• 2008

Social Web is turning current Web into social platform for knowing people and sharing information. This paper takes major social tagging systems as examples, namely delicious, flickr and youtube, to analyze the social phenomena in the Social Web in order to identify the way of mediating and linking social data. A simple Tag Ontology (TO) is proposed to integrate different social tagging data and mediate and link with other related social metadata. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tag ontology is also suggested as an applying field.

• International Journal of Industrial Entomology and Biomaterials
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• v.17 no.2
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• pp.229-234
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• 2008

We used serial analysis of gene expression (SAGE) approach to derive a profile of expressed genes of the posterior silk glands (PSG) and to create a reference for understanding gene cluster related to the mechanism of silk protein synthesis in the silkworm, Bombyx mori. We constructed a 3' SAGE library from the PSG of the fifth instar larvae of the silkworm. In total we obtained 2,406 SAGE tags, of which 682 were unique tags. Sorted by tag count number, 27 (4%) unique tags were significantly more abundant genes (ten or more times), whereas 445 (65%) unique tags were detected as single copies. The annotation of 682 unique SAGE tags revealed that 462 (68%) of the SAGE tag sequences represented known genes, whereas 220 (32%) of the tag sequences had no matches in SAGE map and silkworm EST databases. Of the 682 SAGE tags, the most abundant tag sequences were that of the fibroin light chain gene and the silk protein P25. In addition, we compared two relative abundance results of the SAGE and the EST approaches to verify whether their transcript quantitative aspects are significant or not. The comparative results of relative abundances of the fibroin H-, L- chain and P25 glycoprotein genes indicated that the quantitative approach based on SAGE tags is effective for quantitative cataloging and comparison of expressed genes in same organs. The SAGE tag information reported in this study would be useful for researchers in the field to analyze genes associated with silk processing mechanisms of insects.

• Asia Marketing Journal
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• v.19 no.1
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• pp.1-17
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• 2017

Cause-related Marketing (CrM) has become an increasingly popular marketing approach over the past two decades. However, neither researchers nor organizations fully understand the determinants of a successful CrM partnership. This research fills this gap. Specifically, we employ the schema theory to explore circumstances in which the CrM alliance cannot achieve a success. We use a theoretical modeling approach to report that, when consumers' typicality-based cognitive process is assumed, the CrM activity with the partners' more-discrepant attribute profile cannot be evaluated favorably, but the attribute-level uncertainty about the CrM alliance is less likely to feedback to the two partners. Furthermore, we argue that, under the schema-plus-tag model, consumers may not like the CrM program with a similar attribute profile. Therefore, this CrM approach may fail. To our knowledge, we are the first to apply the schema theory to explain how a CrM alliance can achieve a success.

• Journal of KIISE
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• v.41 no.12
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• pp.1117-1125
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• 2014

Folksonomy data, which is derived from social tagging systems, is a useful source for understanding a user's intention and interest. Using the folksonomy data, it is possible to create an accurate user profile which can be utilized to build a personalized search system. However there are limitations in some of the traditional methods such as Vector Space Model(VSM) for user profiling and similarity computation. This paper suggests a novel method with graph-based user and document profile which uses the proximity information of query terms to improve personalized search. We demonstrate the performance of the suggested method by comparing its performance with several state-of-the-art VSM based personalization models in two different folksonomy datasets. The results show that the proposed model constantly outperforms the other state-of-the-art personalization models. Furthermore, the parameter sensitivity results show that the proposed model is parameter-free in that it is not affected by the idiosyncratic nature of datasets.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.91-101
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• 2011

This research aims to suggest web-based 3D face modeling system for hairline modification surgery. As public interests in beauty regarding face escalate with era of wide persoanl mobile smart iCT devices, need for medical information system is urgent and increasing demand. This research attempted to build 3D facing modeling library deploying conventional technology and proprietary software available. Implications from the our experiment found that problems and requirement for developing new web based standard. We suggest new system from our experiment and literature review regarding relevant technologies. Main features of our suggested systems is based on studies regarding hair loss treatment such as medical science, beauty studies and information technology. This system processes input images of 2D frontal and profile pictures of face into 3D face modeling with mesh-data. The mesh data is compatible with web standard technology including SVG and Canvas Tag supported natively by HTML5.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.3
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• pp.349-360
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• 2019

This study analyzes the trends of recommendation services for customized fashion styles in relation to artificial intelligence. To achieve this goal, the study examined filtering technologies of collaborative, content based, and deep-learning as well as analyzed the characteristics of recommendation services in the users' purchasing process. The results of this study showed that the most universal recommendation technology is collaborative filtering. Collaborative filtering was shown to allow intuitive searching of similar fashion styles in the cognition of need stage, and appeared to be useful in comparing prices but not suitable for innovative customers who pursue early trends. Second, content based filtering was shown to utilize body shape as a key personal profile item in order to reduce the possibility of failure when selecting sizes online, which has limits to being able to wear the product beforehand. Third, fashion style recommendations applied with deep-learning intervene with all user processes of buying products online that was also confirmed to penetrate into the creative area of image tag services, virtual reality services, clothes wearing fit evaluation services, and individually customized design services.

• Horticultural Science & Technology
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• v.33 no.5
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• pp.737-750
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• 2015

This study was carried out to construct a DNA profile database for 102 watermelon cultivars through the comparison of polymorphism level and genetic relatedness using genomic microsatellite (gMS) and expressed sequence tag (EST)-microsatellite (eMS) markers. Sixteen gMS and 10 eMS primers showed hyper-variability and were able to represent the genetic variation within 102 watermelon cultivars. With gMS markers, an average of 3.63 alleles per marker were detected with a polymorphism information content (PIC) value of 0.479, whereas with eMS markers, the average number of alleles per marker was 2.50 and the PIC value was 0.425, indicating that eMS detects a lower polymorphism level compared to gMS. Cluster analysis and Jaccard's genetic distance coefficients using the unweighted pair group method with arithmetic average (UPGMA) based on the gMS, eMS, and combined data sets showed that 102 commercial watermelon cultivars could be categorized into 6 to 8 major groups corresponding to phenotypic traits. Moreover, this method was sufficient to identify 78 out of 102 cultivars. Correlation analysis with Mantel tests for those clusters using 3 data sets showed high correlation ($r{\geq}0.80$). Therefore, the microsatellite markers used in this study may serve as a useful tool for germplasm evaluation, genetic purity assessment, and fingerprinting of watermelon cultivars.

• Journal of the Korea Safety Management & Science
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• v.10 no.4
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• pp.189-198
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• 2008

At present, companies write daily work record or use bar-code in order to collect distribution flow data in real time. However, it needs additional works to check the record or read the bar-code with a scanner. In this case, human error could decrease accuracy of data and it would cause problems in reliability. To solve this problem, RFID (Radio Frequency Identification) is introduced in many automatic recognition sector recently. RFID is a technology that identification data is inserted into micro-mini IC chip and recognize, trace, and manage object, animal, or person using wireless frequency. This is being emerged as the core technology in future ubiquitous environment. This study is intended to suggest RFID application method in franchise business. Traceability and visibility of individual product are supplied based on EPCglobal network. It includes DW system which supplies various assessment data about product in supply chain, financial transaction system which is based on product transaction and position information, and RFID middleware which refines and divides product data from RFID tag. With the suggested application methods, individual product's profile data are supplied in real time and it would boost reliability to customer and make effective cooperation with existing operation systems (SCM, CRM, and e-Business) possible.