• Korean Journal of Soil Science and Fertilizer
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• v.35 no.2
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• pp.105-111
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• 2002

To find out the optimum nitrogen fertilization levels for the leafy vegetables in volcanic ash soils of Cheju island, fertilization effects on chinese cabbage chinese and cabbage were investigated through pot and field experiments. In pot experiment conducted with two volcanic ash soils of Cheju island, optimum rates of nitrogen fertilizer was ranged from 294 to $331kg\;ha^{-1}$ for chinese cabbage. At field experiment with one volcanic soil, the optimum N fertilizer was $331kg\;ha^{-1}$. On the basis of soil organic matters, fertilizer recommendation formula for cabbage, could be established by using 1.03 of comparison factors (F) compared with chinese cabbage : y=344.54-0.285x for chines cabbage, y= 354.88-0.294x for cabbage, where y is the recommendation amount of nitrogen fertilizer with x g $kg^{-1}$ of organic matter in soil. Actual optimum rate of nitrogen fertilizer for chinese cabbage under field condition was much more similar to the value caluculated by the revised nitrogen recommendation formula than the amount of nitrogen fertilizer recommended by the current formula in volcanic ash soil.

• Korea Journal of Hospital Management
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• v.21 no.3
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• pp.11-21
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• 2016

The purpose of this study is to investigate the degree of difficulty and turnover of nurses working in the field and to derive clinical work activation and supply policy improvements. Data was collected from December, 2014 to January, 2015, from 23 hospitals, and participants were 3,887 nurses working in the field, Survey details : the difficulty of the clinical work of nurses working in hospitals, turnover intentions status and policy proposals for clinical research work enabled General characteristics, difficulties in clinical working, turnover intention and clinical work plan activation are frequency analysis. The difference between each of the variables in accordance with the general characteristics are one-way ANOVA analysis, Correlation analysis of the variables is also a Pearson correlation coefficients. 'difficulties in clinical working' was a statistically significant difference depending on the type of hospital, nursing class, number of beds, location, age, position, employment, gender, working form, working department, salary, career, and degree level. 'turnover intention' was a statistically significant difference depending on nursing rate, number of beds, region, age, position, sex, shifts, departments, annual income, and career. 'policy recommendation' was a statistically significant difference depending on type of hospital, nursing rate, age, position, employ, shifts, departments, annual income, degree level and career 'difficulties in clinical working' is 'turnover intention' (p<.001), 'policy recommendations' (p<.001) and had a significant positive correlation. and 'turnover intention' had a "policy recommendation" significant positive correlation with the relationship (p<.001) The most difficulties point of the nurses working in the field are the environment, such as shift, urgent and dangerous. Major policy proposals are improve treatment such as wages, and welfare.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.600-602
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• 2015

WHO(World Health Organization) as specified due to the global epidemic of obesity in the nation and the social costs associated with health increase. If treating diseases of the existing research targets the medical field with increasing interest in the welfare and well-being sector due to the improvement in earnings, and gradually change to advance the prevention and management. In this paper, we consider these social changes, we propose a personalized recommendation system fitness. This makes it possible that the recommendation is effective to the movement by the movement mechanism by which user. Mobile sensor is overcome by software and having hardware limitations for this purpose, proposes an optimized sensor control mechanism.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.61-77
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• 2003

Due to the wide expansion of the internet, people can freely get information what they want with lesser efforts. However without adequate forms or rules to follow, it is getting more and more difficult to get necessary information. Because of seemingly chaotic status of the current web environment, it is sometimes called "Dizzy web" The user should wander from page to page to get necessary information. Therefore we need to construct system which properly recommends appropriate information for general user. The representative research field for this system is called Recommendation System(RS), The collaborative recommendation system is one of the RS. It was known to perform better than the other systems. When we perform the web user modeling or other web-mining tasks, the continuous feedback data is very important and frequently used. In this paper, we propose a collaborative recommendation system which can deal with the continuous feedback data and tried to construct the web page prediction system. We use a sojourn time of a user as continuous feedback data and combine the traditional model-based algorithm framework with the Support Vector Regression technique. In our experiments, we show the accuracy of our system and the computing time of page prediction compared with Pearson's correlation algorithm.algorithm.

• Journal of Information Management
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• v.41 no.4
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• pp.227-249
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• 2010

Scholarly information has increased tremendously according to the development of IT, especially the Internet. However, simultaneously, people have to spend more time and exert more effort because of information overload. There have been many research efforts in the field of expert systems, data mining, and information retrieval, concerning a system that recommends user-expected information items through presumption. Recently, the hybrid system combining a content-based recommendation system and collaborative filtering or combining recommendation systems in other domains has been developed. In this paper we resolved the problem of the current recommendation system and suggested a new system combining collaborative filtering and Naive Bayes Classification. In this way, we resolved the over-specialization problem through collaborative filtering and lack of assessment information or recommendation of new contents through Naive Bayes Classification. For verification, we applied the new model in NDSL's paper service of KISTI, especially papers from journals about Sitology and Electronics, and witnessed high satisfaction from 4 experimental participants.

• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.121-148
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• 2023

The purpose of this study is to propose a scholarly paper recommendation system based on metadata attribute similarity with excellent performance. This study suggests a scholarly paper recommendation method that combines techniques from two sub-fields of Library and Information Science, namely metadata use in Information Organization and co-citation analysis, author bibliographic coupling, co-occurrence frequency, and cosine similarity in Bibliometrics. To conduct experiments, a total of 9,643 paper metadata related to "inequality" and "divide" were collected and refined to derive relative coordinate values between author, keyword, and title attributes using cosine similarity. The study then conducted experiments to select weight conditions and dimension numbers that resulted in a good performance. The results were presented and evaluated by users, and based on this, the study conducted discussions centered on the research questions through reference node and recommendation combination characteristic analysis, conjoint analysis, and results from comparative analysis. Overall, the study showed that the performance was excellent when author-related attributes were used alone or in combination with title-related attributes. If the technique proposed in this study is utilized and a wide range of samples are secured, it could help improve the performance of recommendation techniques not only in the field of literature recommendation in information services but also in various other fields in society.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1355-1362
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• 2016

Recently, the marine leisure are being emphasized with improving the quality of life style by increased income and spare time. Also, there is a increasement of people's interest in marine leisure activities. But resources and facilities do not grow in proportion to the quantitative growth of the current marine leisure industry. Besides, a leisure ship operator tends to choose a simple or familiar route of the local area rather than a new leisure routes which are not explored due to lack of accessible areas information. This paper proposes a routes recommendation system in order to solve above problems based on marine resource database. The databases have been constructed through investigation and analysis of navigational information such as environmental conditions including weather conditions and sea status, field of marine leisure activities, tourist attractions and natural landscape, and marine leisure prohibited areas. Therefore we have developed and implemented the route recommendation system that provides various information necessary to route operation of leisure boats.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

• 한국체육학회지인문사회과학편
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• v.56 no.1
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• pp.53-65
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• 2017

This study was conducted to examine how marine sport event visitors' satisfactions and city image effect on revisit and recommendation. To do this, we analyzed the socio-statistical characteristics of spectators, and examined the influence relationship among city image, satisfaction, revisit and recommendation intention. The results were as follows: First, 'family and relatives' as the companions of spectators of yacht competition were the most, and the route of acquiring the competition information was 'the other', in other words, that most of the respondents learned directly in the field, and 'car' as transportation means was the most. In addition, "local tourism" as watching purposes was the most and 'the day' as the period of stay was the most. Second, the image and satisfaction of spectators of yacht competition showed significant influence on revisit and recommendation intention. Specifically, program satisfaction and program agent satisfaction, which are a sub-factors of satisfaction, have a significant positive effect on revisit and recommendation intention. Through these research results, we were able to confirm inadequacy(lack of attractiveness, connections and public relations) of this event, and see what parts should be improved in order to be born again a sustainable event.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.63-70
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• 2022

Fashion is a field in which personal taste acts as the first criterion for purchase, and it is being refined as an important strategy to increase purchase conversion on mobile. Although related studies have been conducted, there are insufficient studies to confirm this according to the detailed purchasing journey of consumers. The purpose of this study is to examine whether the evaluation of user experience factors of personalized recommendation service differs by purchase journey, and to reveal whether it affects purchase intention and satisfaction. Variety, reliability, and convenience showed a significant difference at the level of 0.001% and usefulness at the level of 0.05%. Satisfaction levels were different for each stage, such as novelty and usefulness in the cognitive and interest stage, and high reliability and diversity in the search stage. It has theoretical significance in that it enhances the understanding of the purchase journey by revealing that there is a difference in user evaluation of the personalized recommendation service, and it has practical significance in that it suggests the direction of improvement of the personalized recommendation service strategy. If research on effectiveness is conducted in the future, it will be able to contribute to an advanced strategy.