• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.1
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• pp.66-74
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• 2005

This survey was conducted to assess the consumer perceptions of food-related hazard in 500 housewives from all over Korea. The subjects were selected by stratified random sampling method. The survey was performed using structured questionnaire through telephone interview by skilled interviewers. The results showed that 34.6% of the respondents felt secure and were not concerned about food safety, and 65.4% were concerned about food safety. Logistic regression analysis showed that the increasing concern on food brands, food additives (such as food preservatives and artificial color), and imported foodstuffs indicated the current increasing concern on food safety. Other related factors indicating the increasing concern on food safety were education level and care for children's health. The respondents who cared about food safety expressed a high degree of concern on processed foodstuffs such as commercial boxed lunch (93.3%), imported foods (92.7%), fastfoods (89.9%), processed meat products (88.7%), dining out (85.6%), cannery and frozen foods (83.5%), and instant foods (82.0%). The lowest degree of concern was on rice. All the respondents perceived that residues of chemical substances such as pesticides and food additives, and endocrine disrupters were the most potential food risk factors, followed by food-borne pathogens, and GMOs (Genetically Modified Organisms). However, these results were not consistent with scientific judgment. Therefore, more education and information were needed for consumers' awareness of facts and myths about food safety. In addition, the results showed that consumers put lower trust in food products information such as food labels, cultivation methods (organic or not), quality labels, and the place of origin. Nevertheless, the respondents expressed their desire to overcome alienation, and recognized the importance of knowing of the origin or the producers of food. They identified that people who need to take extreme precautions on food contamination were the producers, government officials, food companies, consumers, the consumer's association, and marketers, arranged in the order of highest to lowest. They also believed that the production stage of agriculture was the most important step for improving the level of food safety Therefore, the results indicated that there is a need to introduce safety systems in the production of agricultural products, as follows: Good Agricultural Practice (GAP), Hazard Analysis and Critical Control Point (HACCP), and Traceability System (75).

• Journal of Animal Science and Technology
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• v.52 no.2
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• pp.91-96
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• 2010

The purpose of the study was to develop an optimum SNP marker set to be utilized for domestic pork traceability. The study tested 51 SNP markers analyzed for origin of farm to be determined from genotypes of offspring and parents in pigs. With the simulation data through random mating population (PI), half sib mating population ($PI_{half-sib}$) and full sib mating population ($PI_{sibs}$), probability of identical genotypes were analyzed as $5.63{\times}10^{-33}$, $4.35{\times}10^{-15}$ and $1.32{\times}10^{-15}$, respectively. The 51 SNP markers also had 100% accuracy for parental determination. These results suggest that if the pig breeding stock is genotyped with the 51 SNP markers, the genotype information of individual offspring can be checked for farm origins by tracing parental sow and sire. Therefore, these SNP markers will be useful to trace the pork from production to consumption in pigs.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.12
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• pp.1915-1926
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• 2008

Although we know that images of a country or an industry are influential factors in product evaluation, there has been little discussion about the channels through which consumers form a country or an industry image. Guided by the assumption that the image of the Korean fashion industry conveyed through U.S. media will likely affect the evaluation of Korean fashion products, we decided to examine articles published between January 1998 and June 2008 in Woman's Wear Daily(WWD), a prestigious U.S. daily trade newspaper covering all aspects of the national and international fashion business. By using the "Korean", we found 329 relevant articles. Through content analysis, we identified the aspects of the Korean fashion industry that have been considered salient to U.S. fashion media professionals. We set up categories based on the contents of the articles that discussed segments of the supply chain of the fashion industry. We found more comments on the Korean fashion industry as fiber and fabrics supplier or apparel manufacturer than in any other categories, which reflects that South Korea has been traditionally one of the most attractive sourcing countries for the U.S. fashion business. We identified significantly less coverage on the designing, branding, marketing, and retailing aspects of the Korean fashion industry. Due to economic boom in Korea, the country's fashion industry is recognized as having a highly fashion-conscious market that can afford the world's premium brands. However, the industry is viewed as being rather vulnerable to changes in the macro economic environment.

• Information Systems Review
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• v.22 no.1
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• pp.17-31
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• 2020

With the start of the fourth industrial revolution era, technologies of various fields are merged and new types of technologies and products are being developed. In addition, the importance of the registration of intellectual property rights and patent registration to gain market dominance of them is increasing in oversea as well as in domestic. Accordingly, the number of patents to be processed per examiner is increasing every year, so time and cost for prior art research are increasing. Therefore, a number of researches have been carried out to reduce examination time and cost for patent-pending technology. This paper proposes a method to calculate the degree of similarity among patent documents of the same priority claim when a plurality of patent rights priority claims are filed and to provide them to the examiner and the patent applicant. To this end, we preprocessed the data of the existing irregular patent documents, used Word2vec to obtain similarity between patent documents, and then proposed recommendation model that recommends a similar patent document in descending order of score. This makes it possible to promptly refer to the examination history of patent documents judged to be similar at the time of examination by the examiner, thereby reducing the burden of work and enabling efficient search in the applicant's prior art research. We expect it will contribute greatly.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.