• Journal of Korean Library and Information Science Society
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• v.12
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• pp.133-160
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• 1985

In college or university libraries, the data of dissertations is one of the important academic information data. But traditional cataloging rules of dissertations and the method of operation is not only didn't unify but also fit to actual circumstances of library. This reason make librarians waste of time and the situation about piling up of unprocessed data don't make library services properly. In this papers, to analyze the above mentioned problems, I have investigated with questionaries about actual examples of construction methods of call number and item describing form of dissertations which is being used now in every college library in Korea. With the replied 61 questionaries which was compared with and investigated to merits and faults about library itself, I have showed revision ways. The characteristics of the revision ways are the followings. 1) Deleted informal and unmeaning descriptions and simplified describing. 2) Recommended to operating one or two kinds describing items which are only necessary. 3) Made call number specifically and then it make us easy to identify data and simultaneously call number can do its original action. 4) shelving range of data is made to colleges, years, departments, majors but in same departments, author's name is recommended by Korean alphabetical ordering. Besides, for proper and systematic management and operating of data. It must be necessary to recommend that reading systems of closed shelves type, incoming and outgoing and shelving by fulltime exclusive charged librarian, separate management of items and data are recommended.

• Asia pacific journal of information systems
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• v.20 no.1
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• pp.81-99
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• 2010

Lately, in consumers' markets the number of new items is rapidly increasing at an overwhelming rate while consumers have limited access to information about those new products in making a sensible, well-informed purchase. Therefore, item providers and customers need a system which recommends right items to right customers. Also, whenever new items are released, for instance, the recommender system specializing in new items can help item providers locate and identify potential customers. Currently, new items are being added to an existing system without being specially noted to consumers, making it difficult for consumers to identify and evaluate new products introduced in the markets. Most of previous approaches for recommender systems have to rely on the usage history of customers. For new items, this content-based (CB) approach is simply not available for the system to recommend those new items to potential consumers. Although collaborative filtering (CF) approach is not directly applicable to solve the new item problem, it would be a good idea to use the basic principle of CF which identifies similar customers, i,e. neighbors, and recommend items to those customers who have liked the similar items in the past. This research aims to suggest a hybrid recommendation procedure based on the preference boundary of target customer. We suggest the hybrid recommendation procedure using the preference boundary in the feature space for recommending new items only. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. Customers' preferences and characteristics of items including new items are represented in a feature space, and the scope or boundary of the target customer's preference is extended to those of neighbors'. The new item recommendation procedure consists of three steps. The first step is analyzing the profile of items, which are represented as k-dimensional feature values. The second step is to determine the representative point of the target customer's preference boundary, the centroid, based on a personal information set. To determine the centroid of preference boundary of a target customer, three algorithms are developed in this research: one is using the centroid of a target customer only (TC), the other is using centroid of a (dummy) big target customer that is composed of a target customer and his/her neighbors (BC), and another is using centroids of a target customer and his/her neighbors (NC). The third step is to determine the range of the preference boundary, the radius. The suggested algorithm Is using the average distance (AD) between the centroid and all purchased items. We test whether the CF-based approach to determine the centroid of the preference boundary improves the recommendation quality or not. For this purpose, we develop two hybrid algorithms, BC and NC, which use neighbors when deciding centroid of the preference boundary. To test the validity of hybrid algorithms, BC and NC, we developed CB-algorithm, TC, which uses target customers only. We measured effectiveness scores of suggested algorithms and compared them through a series of experiments with a set of real mobile image transaction data. We spilt the period between 1st June 2004 and 31st July and the period between 1st August and 31st August 2004 as a training set and a test set, respectively. The training set Is used to make the preference boundary, and the test set is used to evaluate the performance of the suggested hybrid recommendation procedure. The main aim of this research Is to compare the hybrid recommendation algorithm with the CB algorithm. To evaluate the performance of each algorithm, we compare the purchased new item list in test period with the recommended item list which is recommended by suggested algorithms. So we employ the evaluation metric to hit the ratio for evaluating our algorithms. The hit ratio is defined as the ratio of the hit set size to the recommended set size. The hit set size means the number of success of recommendations in our experiment, and the test set size means the number of purchased items during the test period. Experimental test result shows the hit ratio of BC and NC is bigger than that of TC. This means using neighbors Is more effective to recommend new items. That is hybrid algorithm using CF is more effective when recommending to consumers new items than the algorithm using only CB. The reason of the smaller hit ratio of BC than that of NC is that BC is defined as a dummy or virtual customer who purchased all items of target customers' and neighbors'. That is centroid of BC often shifts from that of TC, so it tends to reflect skewed characters of target customer. So the recommendation algorithm using NC shows the best hit ratio, because NC has sufficient information about target customers and their neighbors without damaging the information about the target customers.

• Journal of Information Technology Services
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• v.20 no.4
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• pp.129-140
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• 2021

Recently, interest in AI tutors is rising as a way to bridge the educational gap in school settings. However, research confirming the effectiveness of AI tutors is lacking. The purpose of this study is to explore how effective learner characteristics and recommended item learning activities are in predicting learner's academic performance in an adaptive online learning environment. This study proposed the hypothesis that learner characteristics (prior knowledge, midterm evaluation) and recommended item learning activities (learning time, correct answer check, incorrect answer correction, satisfaction, correct answer rate) predict academic achievement. In order to verify the hypothesis, the data of 362 learners were analyzed by collecting data from the learning management system (LMS) from the perspective of learning analytics. For data analysis, regression analysis was performed using the regsubset function provided by the leaps package of the R program. The results of analyses showed that prior knowledge, midterm evaluation, correct answer confirmation, incorrect answer correction, and satisfaction had a positive effect on academic performance, but learning time had a negative effect on academic performance. On the other hand, the percentage of correct answers did not have a significant effect on academic performance. The results of this study suggest that recommended item learning activities, which mean behavioral indicators of interaction with AI tutors, are important in the learning process stage to increase academic performance in an adaptive online learning environment.

• The Journal of KAIRB
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• v.6 no.1
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• pp.17-31
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• 2024

Purpose: This study aims to develop preliminary items for measuring the perceived service quality of clinical trials among participants and to verify content validity. Methods: This study was designed as a methodological study. A conceptual framework was established based on Brady and Cronin's hierarchical model, and preliminary items were prepared through translation-back-translation, a review of existing instruments, and in-depth interviews with clinical trial participants and clinical research coordinators. The final items were completed through content validity testing by experts and a review of items by clinical trial participants for the prepared preliminary items. Results: Through this study, a set of 58 items across four domains (quality of interaction with researchers, the physical environment, performance procedures, and performance results) and 9 components (information·education·communication, trust, respect for participant preferences, securing facilities and space, accessibility, comfortability, informed consent, coordination of care, subjective understanding of clinical trials) on the service quality of clinical trials were completed. The scale content validity index of all preliminary items was 0.96, meeting the recommended standards. The individual-item content validity index also meets the recommended criteria for most items, excluding four items. Conclusion: This study holds significance in developing items to measure the quality of clinical trial execution from the perspective of participants. By verifying the reliability and validity of these items through subsequent research, it is expected that they can be utilized as a valuable instrument to devise strategies for improving the quality of clinical trials.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.129-137
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• 2019

Recommendation systems help users by suggesting items such as products, services, and information. However, most research on recommendation systems has not considered people's moods although the appropriate contents recommended to people would be changed by people's moods. In this paper, we propose a situation-based recommendation system which exploits people's mood. The proposed scheme is based on the fact that the mood of a user is changed frequently by the surrounding environments such as time, weather, and anniversaries. The environments are defined as feature identifications, and the rating values on items are stored as feature identifications at a database. Then, people can be recommended diverse items according to their environments. Our proposed scheme has some advantages such as no problem of cold start, low processing overhead, and serendipitous recommendation. The proposed scheme can be also a good option as of assistance to other recommendation systems.

• Journal of the Korean Society of Food Culture
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• v.11 no.5
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• pp.643-649
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• 1996

This study was performed to investigate the adequacy of dietary fat and protein intakes among female college students. Daily intakes of energy, fat, protein, major amino acids and other nutrients were measured in 52 female college students. Daily energy intake was 75.8% of the recommended intake. Fat and protein consist 19.2% and 16.7% of the total calorie, respectively. The average protein consumption per day was 105% of the recommended intake. Essential amino acids intakes were more than the recommended amounts which appears in the 6th edition of Recommended Dietary Allowances for Koreans. However, when the intake of each essential amino acid was compared to the recommended amino acid requirement pattern, these subjects did not meet the estimated requirements. There was a highly significant correlation between daily protein intake and lipid intake implying the major sources of protein in the diet were also major sources of fat. Daily intakes of dietary fiber, vitamin C, iron, and phosphorous were above the recommended levels of intake. However, blood hemoglobin concentration was marginal indicating dietary iron consumption is not a good marker for iron status. Also, calcium intake was only 63.5% of the recommended intake. Therefore, these results imply that main problems for these subjects are low energy consumption, low calcium intake, and the quality of protein. However, as opposed to the hypothesis, the main energy sources were not the food items high in saturated fats such as instant foods, which should be emphasized further.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.101-115
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• 2008

Combining collaborative filtering with some other technique is most common in hybrid recommender systems. As many recommended items from collaborative filtering seem to be similar with respect to content, the collaborative-content hybrid system suffers in terms of quality recommendation and recommending new items as well. To alleviate such problem, we have developed a novel method that uses a diversity metric to select the dissimilar items among the recommended items from collaborative filtering, which together with the input when fed into content space let us improve and include new items in the recommendation. We present experimental results on movielens dataset that shows how our approach performs better than simple content-based system and naive hybrid system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.221-226
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• 2021

Collaborative filtering based recommender systems recommend user-preferrable items based on rating history and are essential function for the current various commercial purposes. In order to determine items to recommend, prediction of preference score for unrated items is estimated based on similar rating history. Previous studies usually employ two methods individually, i.e., similar user based or similar item based ones. These methods have drawbacks of degrading prediction accuracy in case of sparse user ratings data or when having difficulty with finding similar users or items. This study suggests a new rating prediction method by integrating the two previous methods. The proposed method has the advantage of consulting more similar ratings, thus improving the recommendation quality. The experimental results reveal that our method significantly improve the performance of previous methods, in terms of prediction accuracy, relevance level of recommended items, and that of recommended item ranks with a sparse dataset. With a rather dense dataset, it outperforms the previous methods in terms of prediction accuracy and shows comparable results in other metrics.

• Fashion & Textile Research Journal
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• v.21 no.5
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• pp.627-639
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• 2019

This study analyzes differences between the results of 3D direct measurements and automated measurements for Korean elderly females according to age groups, side somatotype, and BMI groups. This study compares the measurement differences of the direct and the 3D automated measurements for women between the ages of 70 to 85, according to age group, BMI group, and side somatotype. A comparison of the results of the direct measurement and the 3D automated measurements for elderly women show that a meaningful discrepancy exists for 29 items out of 33 items. Furthermore, the results of comparing the average error tolerance recommended by ISO20685 shows that 30 items out of 33 items exceeded ISO recommendations. The results of the automated measurement program shows a higher degree of accuracy for straight postures; however, this unsuitable for postures of elderly women with a changed somatotype. The analysis results of the measurement difference indicate the suitability of the automatic measurement programs is found to be high for stood postures, while problems seem to exist on several items along with an automated program is not appropriately used due to posture and part of body changes for elderly women. Therefore, it is recommended to develop an algorithm, that reflects the body changes of elderly women first and then upgrade the automated program equipped with a measurement size method. It is hoped that the study results can be utilized as base data for improving the automated measurement program.

• Journal of Korean Academy of Nursing
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• v.37 no.3
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• pp.324-333
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• 2007

Purpose: The purpose of this study was to develop and evaluate a quality of life scale for Korean patients with cancer (C-QOL). Methods: The C-QOL was developed and validated as follows, item generation, pilot study, and psychometric tests. A total of 337 patients diagnosed with stomach, liver, lung, colon, breast, or cervix cancer were recruited. The patients were asked to complete the preliminary questionnaire comprising the content-validated items, the SF-36, and the ECOG performance status. The obtained data was analyzed using descriptive statistics, factor analysis, multidimensional scaling (MDS), multitrait/multi-item matrix, ANOVA, t-test, and Cronbach's alpha. Results: Preliminarily twenty-six items were generated through content validity and a pilot study. Factor analysis and MDS extracted a total of 21 items with a 5-point Likert-type scale (C-QOL). The C-QOL included five subscales: physical status (6 items), emotional status (6 items), social function (3 items), concern status (2 items), and coping function (4 items). The C-QOL established content validity, construct validity, item convergent and discriminant validity, known-groups validity, reliability, and sensitivity. Conclusion: The Newly developed C-QOL is an easily applicable instrument which established psychometric properties and reflected Korean culture. It is recommended for further study to examine the responsiveness of the C-QOL using a longitudinal research design.