• 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 the Korea Safety Management & Science
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• v.13 no.4
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• pp.193-199
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• 2011

In the past annual recycling obligation rate calculation of Extended Producer Responsibility (EPR) system, it was difficult to operate the system efficiently, because responsible producers passively participated in the scheme only bent on achieving annual obligation without long-term plan. Thus, a new scheme of long-term recycling obligation rate began to be established every five year from 2008 in order to give the basis for the notice of annual specific operation standard and recycling obligation, thereby helping responsible producers to make a preparation with a plan and giving expectation of active operation of the scheme. However, in the operation of long-term recycling target program, while the development of prediction models and the evaluation for existing items has been conducted in various ways, applications for a new target items and the evaluation are quite insufficient. Therefore, in this study, problems in implementing long-term recycling goal of new target items will be examined, and more objective and rational long-term recycling rate calculation and the operation standard will be proposed. Thus, the long-term recycling target will play a role as a pacemaker to steadily improve the recycling performance of target items, and responsible producers will be expected to increase the achievement with the realistic capacity.

• Journal of Korean Society for Quality Management
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• v.41 no.3
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• pp.413-421
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• 2013

Purpose: System availability and life cycle cost are often used to evaluate the system performance and is influenced by the operation and maintenance characteristic. In this paper, we propose the method to improve life cycle cost and satisfy the target availability through redundancy allocation. Methods: We consider the redundancy is available at all items in multi level system. Thus, we assume that sub-assembly, module, components can be duplicated. Simulation and genetic algorithm are employed to optimize redundancy allocation. Results: Target availability is higher, the life cycle cost is increased. In addition, the items for redundancy are selected at higher level in multi level system if target availability is higher. Conclusion: We could know that target availability affects the duplication number of items and the selection of redundancy items. For further study, we will consider new optimization algorithms to compare with the proposed GA algorithm and improve optimization performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.1-19
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• 2021

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.

• Korean Journal of Community Nutrition
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• v.19 no.3
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• pp.274-282
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• 2014

Objectives: The objective of this study was to revise the target pattern in food guidance system for adolescents' balanced menu planning. Methods: The food groups in the target pattern were divided into detailed food items, and intake number were assigned to each food items based on the revised standard food composition table. The validity of revised target pattern was examined. Menu planning according to the revised target pattern was made available to 305 male and female middle school students and the nutritional assessment of the menu plan were carried out using SPSS WIN 12.0. Results: The energy contents, energy contribution ratios of carbohydrate, fat, and protein, and 4 minerals' and 6 vitamins' contents of the revised target pattern were adequate. The average energy contents of the menu planned according to revised target pattern were 400~500 kcal higher than that of the revised target pattern when the revised standard food composition was applied. The energy contribution ratios of fat were 28.9%, close to maximum of acceptable macronutrient distribution range (AMDR) (30%), and that of carbohydrate were 54.5%, lower than minimum of AMDR (55%). The nutrient adequacy ratios (NARs) of calcium and vitamin C were less than 1.0. According to index of nutritional quality (INQ) of food items, kimchi, milk dairy products, and soybean curd were energy efficient source for calcium, kimchi, fruit, vegetable and seaweed were energy efficient source for vitamin C, with INQ of food items were higher or close to 2.0. Kimchi was the best energy efficient source of calcium and vitamin C. Conclusions: Revised target pattern based on the adolescent's foods intake was not good enough for balanced menu planning by adolescents, because what they ate and what they wanted to eat were very much different. Detailed guidance for food selection is necessary in each food items.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.241-249
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• 2002

Making traditional plan of target marketing based on Association Rule has brought restriction to obtain the target of marketing. This paper is to present Inverse Association Rule as a new association rule for target marketing. Inverse Association Rule does not use information about relation between items that customers purchase like Association Rule, but use information about relation between items that customers do not pruchase. By adding Inverse Association Rule to target marketing, we generate new marketing rule to look for new target of marketing. From new marketing rule, this paper is to show direct marketing about target item and indirect marketing about another item associated with target item to sell target item. The reason is that sales of the item associated with target item have an influence on sales of target item.

• Journal of Environmental Impact Assessment
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• v.27 no.1
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• pp.1-16
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• 2018

Selecting target items of EIA(Environmental Impacts Assessment) is very important process in conducting the EIA. In Korea, like other countries, the EIA Council selects the target items before starting the EIA process. However, the assessment items stipulated in the Enforcement Decree of the Environmental Impact Assessment Act is almost wholly applied to most businesses. Thus, the EIA is difficult to carry out reflecting the characteristics of the target business. Additionally, the items of EIA have a structure that is difficult to change, so that the items of EIA is easy to fall apart from the current social needs. Therefore, the purpose of this study is to suggest adjustment of the items of EIA by reflecting the changes in the assessment and social conditions based on analysis of the usage and effectiveness of the current EIA items. In addition, this study would like to propose a improvement of the operating system in order to ensure that EIA items can be selected effectively.

• Journal of the East Asian Society of Dietary Life
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• v.16 no.1
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• pp.77-84
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• 2006

Nowadays, people are more concerned about health food than satisfying their hunger. Therefore, media presents programs related to health food such as dietary food, traditional food, and herbal items. The trend has emphasized the importance of traditional food items and the need for a 'han-bang' menu development The purpose of this study was to identify the perception of herbal items and other food items used in medicinal cuisine in the Seoul Area. Out of 300 questionnaires distributed, 287 were collected and analyzed. Descriptive analysis, factor analysis, ANOVA, and T-test were conducted using SPSS 12.0 for windows. This study identified that the perception of herbal items was influenced by age, education, and wage level. Daily eating habits partially affected on the perception of herbal items. Accordingly, these findings indicate that it is necessary for 'han-bang' menu' development set to target market.

• Korean Journal of Cognitive Science
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• v.25 no.3
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• pp.211-232
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• 2014

People search a certain object or a person so many time in a day. Besides the information about what the target is, perceptual information of the target can influence on the search process. In the current study, using an eye-tracker we aimed to examine whether the perceptual information of previewing target stimuli on the visual search process of the target and the task performance. Participants had to identify the previewing target stimulus presented in the middle of the screen, and then had to search the target among 8 items presented in a circle array, and had to decide whether the size of the target in the search display was same as that of the previewing stimulus. The experimental conditions were divided into 8 within-subject conditions by whether the search display was consisted of all the same size items or different size items (homogeneous search display vs. inhomogeneous search display), by the size of the preview target stimulus, and by the size of the target stimulus in the search display. Research hypothesis is that the size information of the previewing influence on the visual search process of the target and task performance when the items in the search display are in different sizes. In the results of behavioral data analysis, the reaction time showed the main effect of the search display, and the size of the target stimulus in the search display. and the interaction between the size consistency effect of target stimulus and the search display condition. In the results of analysis of eye-movement information, the Initial Saccade to Target Ratio measurement showed the interaction between the size consistency effect of target stimulus and the search display condition as the reaction time measurement did. That is, the size consistency effect of target stimulus only in the inhomogeneous search display condition indicated that participants searched the items in the same size as that of preview target stimulus. Post-hoc analyses revealed that the search and task performance in the inhomogeneous display condition were faster when the target size was consistent, but rather slower when the target size was inconsistent.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.195-209
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• 2003

Making traditional plan of target marketing based on association rule has brought restriction to obtain the target of marketing. This paper is to present inverse association rule as a new association rule for target marketing. Inverse association rule does not use information about relation between items that customers purchase, but use information about relation between items that customers do not purchase. By adding inverse association rule to target marketing, we generate new marketing strategy to look for new target of marketing. There are three steps to apply the marketing strategy proposed by this Paper to target marketing. Firstly, a database is converted to an inverse database. Although inverse association rules can be generated from a database, it is easier to explain inverse association rule in an inverse database than in a database. Secondly, association rules and inverse association rules are generated from inverse database. Finally, two types of rules which are created in the previous steps are applied to target marketing. From new marketing rule, this paper is to show direct marketing about target item and indirect marketing about another item associated with target item to sell target item. The reason is that sales of the item associated with target item have an influence on sales of target item.