• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.658-663
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• 1994

The classification and coding of part for group technology applications continus to be labour intensive and time-consuming process, and therefore much effort is dedicated to the structure and creation of automatic coding systems. IN this paper, Neural networks is used to generate processes-related digit as well as part geometry-related digit of the TS code where part name is provided as input.since part name, which is appropriately designated, provides much information about part geometry and manufacturing processes. THe developed GT recommendation system is integrated with interactive TS coding system and database in order to handle the changes of production environment, such as the change of production part of plant. It is found to recommend codes accurately and promises to be a useful tool for consistent, reliable and convenient coding processes.

• Journal of Information Science Theory and Practice
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• v.12 no.2
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• pp.64-78
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• 2024

Sentiment analysis is one of the promising approaches for developing a point of interest (POI) recommendation system. It uses natural language processing techniques that deploy expert insights from user-generated content such as reviews and feedback. By applying sentiment polarities (positive, negative, or neutral) associated with each POI, the recommendation system can suggest the most suitable POIs for specific users. The proposed study combines two models for POI recommendation. The first model uses bidirectional long short-term memory (BiLSTM) to predict sentiments and is trained on an election dataset. It is observed that the proposed model outperforms existing models in terms of accuracy (99.52%), precision (99.53%), recall (99.51%), and F1-score (99.52%). Then, this model is used on the Foursquare dataset to predict the class labels. Following this, user and POI embeddings are generated. The next model recommends the top POIs and corresponding coordinates to the user using the LSTM model. Filtered user interest and locations are used to recommend POIs from the Foursquare dataset. The results of our proposed model for the POI recommendation system using sentiment analysis are compared to several state-of-the-art approaches and are found quite affirmative regarding recall (48.5%) and precision (85%). The proposed system can be used for trip advice, group recommendations, and interesting place recommendations to specific users.

• Korean Journal of Clinical Pharmacy
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• v.27 no.2
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• pp.113-118
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• 2017

Objective: International institutes such as Global institute for Asthma(GINA), KAAACI(Republic of Korea), NHLBI(USA), BTS(UK) and JSA(Japan) have published guidelines for asthma treatment. The aim of this study was to compare the representatives' international guidelines of pharmacotherapy for pediatric asthma. Methods: The recommendations related to pharmacotherapy for pediatric asthma were extracted from the latest representatives' international guidelines, and comprehensive comparisons were conducted. Results: Major comparison outcomes between international guidelines were evaluated as follows: classification system on severity and pediatric age group, recommendation for inhaled corticosteroid dose, recommendation for pediatric age group of theophylline in mild asthma, and recommendation for pediatric age group of tiotropium in severe asthma. Clinical trials emphasized the adverse effects of theophylline, whereas tiotropium demonstrated beneficial actions for pediatric asthma. Therefore, theophylline was recommended for older patients with persistent asthma, and tiotropium was considered to be suitable for younger patients with severe asthma according to GINA guidelines. Conclusion: These findings address the requirement to harmonize international guidelines of pharmacotherapy in pediatric asthma. In addition, the findings suggest that KAAACI needs to update its pharmacotherapy guidelines of theophylline, tiotropium and other medicines recently approved.

• Journal of the East Asian Society of Dietary Life
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• v.8 no.1
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• pp.1-8
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• 1998

This study was designed to investigate the optimum recommendation level on Gatrodia rhizoma and the effects on the improvement of the lipids in the dietary hypercholesterterolemic rats. Experimental diets mixed with 5% Dioscorea batatas(Group 2), 10% Dioscorea batatas(Group 3), 15% Dioscorea batatas (Group 4) 5% Gastrodia rhizoma (Group 5), 10% Gastrodia rhizoma (Group 6), and 15% Gastrodia rhizoma (Group 7), were administered to the male rats of the Sprague Dawley for 3 weeks. Concentration of total cholesterol in serum was lower in the Gastrodia rhizoma groups than in the other groups, especially total cholesterol concentration of 10% Gastrodia rhizoma(Group 6) was the lowest in the Gastrodia rhizoma groups. Concentration of HDL-cholesterol in serum was higher in the 10% Gastrodia rhizoma and 15% Gastrodia rhizoma than in the other groups. Concentrations of cholesteryl ester, LDL, LDL-cholesterol in serum were the lowest in the 10% Gastrodia rhizoma. Concentration of glucose and activity of GPT in serum were the lowest in the 10% Gastrodia rhizoma group. The activity of GOT in serum was lower in the 10% Gastrodia rhizoma group and 15% Gastrodia rhizoma group than in the other groups. Therefore, we consider that there are effects on the improvement of the lipids in the Gastrodia rhizoma and the optimum recommendation quantity of Gastrodia rhizoma is 10% to quantity of food composition.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.6
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• pp.1010-1024
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• 2016

This study identifies the influence of on/off-line acquaintances' recommendation messages on fashion product attitude and purchase intention on the online purchase of fashion products in two-sided word of mouth situations as well as compares the difference in influence according to bond-base with equidistance. This study was conducted for one month on university students in their 20s who were believed to be active in smartphone use. Out of the collected 174 copies of the questionnaire, 162 copies were used for analysis. The questionnaire was classified into online and offline recommendation messages of an acquaintance. We present two-sided fashion product reviews made similar to the type found in an actual shopping mall web-site. As for analysis, confirmatory factory analysis, structural equation modeling, and multi-group analysis were conducted using AMOS 19.0. The analysis results are as follows. First, on/off-line acquaintances' recommendation messages had significant influences on product attitude in the situation where two-sided reviews on fashion products were presented; however, those messages did not influence purchase intention. Recommendation messages positively increased product attitude and enhanced purchase intention if acquaintances' recommendation messages were mediated between on/off-line acquaintances' recommendation messages and purchase intention. Consequently, a mediating effect on product attitude was revealed. Second, there was no difference between online acquaintances and offline acquaintances in terms of the influence of acquaintances' recommendation messages on product attitude and purchase intention, in the situation where two-sided reviews were presented on online fashion products. Therefore, no control effect according to the type of acquaintance was confirmed.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.153-177
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• 2001

Nowadays, product recommendation is one of the important issues regarding both CRM and Internet shopping mall. Generally, a recommendation system tracks past actions of a group of users to make a recommendation to individual members of the group. The computer-mediated marketing and commerce have grown rapidly and thereby automatic recommendation methodologies have got great attentions. But the researches and commercial tools for product recommendation so far, still have many aspects that merit further considerations. To supplement those aspects, we devise a recommendation methodology by which we can get further recommendation effectiveness when applied to Internet shopping mall. The suggested methodology is based on web log information, product taxonomy, association rule mining, and decision tree learning. To implement this we also design and intelligent Internet shopping mall support system based on agent technology and develop it as a prototype system. We applied this methodology and the prototype system to a leading Korean Internet shopping mall and provide some experimental results. Through the experiment, we found that the suggested methodology can perform recommendation tasks both effectively and efficiently in real world problems. Its systematic validity issues are also discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.587-597
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• 2016

This study is to investigate the influence of leisure experience type and self-efficacy, on subjective well-being in ski resort. The study is to examine the moderating role of leasure experience type (ski related experience and non ski related experience) between self-efficacy and subjective well-being. Further, the relationship between subjective well-being and recommendation intention was investigated. As a result, self-efficacy significantly influenced on subjective well-being (satisfaction of leisure, positive affect). Respondents who are high self-efficacy group have higher subjective well-being (satisfaction of leisure, positive affect) than one who are low self-efficacy group in the ski related leasure experience scenarios. In the non ski related leasure experience scenarios, however, respondents who are low self-efficacy group have higher subjective well-being (satisfaction of leisure, positive affect). Besides, subjective well-being (satisfaction of leisure, positive affect) plays a mediating role between self-efficacy and recommendation intention.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.135-146
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• 2010

In this paper, we propose an Recommendation System for supporting self-directed learning on e-learning marketplace. The key idea of this system is recommendation system using revised collaborative filtering to support marketplace. Exisiting collaborative filtering method consists of 3 stages as preparing low data, building familiar customer group by selecting nearest neighbor, creating recommendation list. This study designs recommendation system to support self-directed learning by using collaborative filtering added nearest neighbor learning course that considered industry and learning level. This service helps to select right learning course to learner in industry. Recommendation System can be built by many method and to recommend the service content including explicit properties using revised collaborative filtering method can solve limitations in existing content recommendation.

• Journal of the Korea Fashion and Costume Design Association
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• v.24 no.2
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• pp.59-72
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• 2022

The online fashion market in the 21st century has shown rapid growth. Against this backdrop, using consumer activity data to provide customized customer services has emerged as a viable business model that draws attention. Algorithm-based personalized recommendation services are a good example. But their application in fashion products has clear limitations. It is not easy to identify consumers' perceptions of the attributes of fashion, which are various, hard to define, and very sensitive to trends. So there is a need to compile data on consumers' underlying awareness and to carry out defined research to increase the utilization of such services in the fashion industry and further engage consumers. This research aims to classify the attributes and types of fashion products and to identify consumers' perceptions of a given situation where a recommendation service is offered. To find out consumers' perceptions of and satisfaction with recommendation services, an online and mobile survey was conducted on women in their 20s and 30s, a group that uses recommendation services frequently. A total of 455 responses were used for analysis. SPSS 28.0 was used, combined with Conjoint Analysis and multiple regression, to analyze data. The study results could provide insights into a better understanding of recommendation services and be used as basic data for companies to identify consumers' preferences and draw up a detailed strategy for market segmentation.