• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.4
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• pp.1-17
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• 2024

This study analyzed the behaviors and satisfaction of park users in nine parks representing first and second-generation new towns, aiming to propose directions for planning new town parks. According to the analysis, park users in new towns mainly visit parks for purposes such as relaxation, strolling, and exercise, often with family, alone, or with friends. They typically spend 1-2 hours in the park and mostly access it on foot. Additionally, satisfaction with park accessibility is high, particularly among pedestrians. Satisfaction survey results indicate that pedestrian pathways, trees and vegetation, water features, rest areas, and cultural facilities have the greatest impact on overall park satisfaction. Playgrounds and sports facilities show relatively lower satisfaction levels, indicating a need for improvement. Furthermore, according to NPS analysis, park users are highly willing to recommend parks, especially with Gwanggyo Lake Park and Dongtan Central Park receiving high recommendation scores. IPA analysis shows that pathways and vegetation are perceived as highly important and satisfactory, while playgrounds and sports facilities are categorized as areas needing improvement. Thus, there is a need to consider improvement strategies for each. Additionally, identifying park users' grievances can lead to creating a better park environment. Finally, concerning the planning direction for new town parks, linear-shaped parks facilitating walking are preferred, with parks preserving natural terrain and forests deemed the most desirable. Based on these results, future city parks, including those in the third-generation new towns, should harmonize with nature and prioritize pedestrian access.

• The Journal of the Korea Contents Association
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• v.7 no.4
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• pp.66-73
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• 2007

Internet is a part of our common life and tremendous information is cumulated. In these trends, the personalization becomes a very important technology which could find exact information to present users. Previous personalized services use content based filtering which is able to recommend by analyzing the content and collaborative filtering which is able to recommend contents according to preference of users group. But, collaborative filtering needs the evaluation of some amount of data. Also, It cannot reflect all data of users because it recommends items based on data of some users who have similar inclination. Therefore, we need a new recommendation method which can recommend prefer items without preference data of users. In this paper, we proposed the apparel coordination system using personalized preference on the semantic web. This paper provides the results which this system can reduce the searching time and advance the customer satisfaction measurement according to user's feedback to system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.543-550
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• 2020

Interest in food recipe retrieval systems has been increasing recently. Most computer-based recipe retrieval systems are searched by cooking name or ingredient name. Since each recipe provides information in different weighing units, recalculations to the desired amount are necessary and inconvenient. This paper introduces a computer system that addresses these inconveniences. The system is a chatbot system, based on web-based recipe recommendations, for users familiar with the use of messenger conversation systems. After selecting the most popular recipes by their names, and pre-processing to extract only information required for the recipes, the system recommends recipes based on the 100,000 data. Recipes are then searched by the names of food ingredients (included and excluded). Recalculations are performed based on the number of servings entered by the user. A satisfaction rate for the systems' recommendations was 90.5%.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.203-214
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• 2020

Nowadays, smart speakers are increasingly personalized and serve as recommendation agents for user. The aim of this study is find out effects of 'Explanation facilities' on transparency, perceived trust, user satisfaction, behavioral intentions of users to reuse, privacy risk, and quality of recommendation in the context of an interact with smart speaker's conversational agents. And we also use measurement for level of privacy concerns to see individuals's level of privacy concerns affected the assessment. The result of this study as follow; First, all measurement variable are significantly related to 'Explanation facilities' Second, perceived trust, privacy risk are significantly related to individual's level of privacy concern. This study found that 'Explanation facilities' could be applied in context of smart speaker and possibility of cognitive dissonance according to the level of privacy concerns.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.1-8
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• 2007

In electronic commerce, it is important for users to recommend the proper item among large item sets with saving time and effort. Therefore, if the recommendation system can be recommended the suitable item, we will gain a good satisfaction to the user. In this paper, we proposed the associative relation clustering split method in the collaborative filtering in order to perform the accuracy and the scalability. We produce the lift between associative items using the ratings data. and then split the node group that consists of the item to improve an efficiency of the associative relation cluster. This method differs the association about the items of groups. If the association of groups is filled, the reminding items combine. To estimate the performance, the suggested method is compared with the K-means and EM in the MovieLens data set.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.65-73
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• 2007

This study devotes to compare the accuracy of Top-N recommendations of items transacted on the web site for customers with the accuracy of rank conformity of the real ratings with estimated ratings for customers preference about items generated from two types of collaborative filtering algorithms. One is Neighborhood Based Collaborative Filtering Algorithm(NBCFA) and the other is Correspondence Mean Algorithm(CMA). The result of this study shows the accuracy of Top-N recommendations and the rank conformity of real ratings with estimated ratings generated by CMA are better than that of NBCFA. It would be expected that the customer's satisfaction in Recommender System is more improved by using the prediction result from CMA than NBCFA, and then Using CMA in collaborative filtering recommender system is more efficient than using NBCFA.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.385-392
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• 2002

Collaborative filtering method for personalization can suggest new items and information which a user hasn t expected. But there are some problems. Not only the steps for calculating similarity value between each user is complex but also it doesn t reflect user s interest dynamically when a user input a query. In this paper, classifying users by their interest makes calculating similarity simple. We propose the a1gorithm for readjusting user s interest dynamically using the profile and Bayesian learning. When a user input a keyword searching for a item, his new interest is readjusted. And the user s profile that consists of used key words and the presence frequency of key words is designed and used to reflect the recent interest of users. Our methods of adjusting user s interest using the profile and Bayesian learning can improve the real satisfaction of users through the experiment with data set, collected in University s library. It recommends a user items which he would be interested in.

• The Journal of Korean Medicine
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• v.40 no.2
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• pp.1-16
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• 2019

Objectives: The purpose of this study was to increase the utilization of Korean Medicine Clinical Practice Guidelines(KMCGP) for ankle sprain by investigating the recognition of guideline developed in 2015 and evaluating the current status of treatment. Methods: An e - mail questionnaire survey was conducted for Korean medicine doctor(K.M.D) registered in Korean Medicine Association. Survey data were analyzed through Excel. Results: The most common Korean medicine treatments used in clinic were acupuncture(adjacent points)(28.5%), cupping therapy(19.7%) and pharmacopuncture(9.8%). The treatments with high patient satisfaction were acupuncture (adjacent points)(27.9%), moxibustion(22.4%) and herbal medicine(10.4%). Herbal medicine(17.9%), tuina(10.7%) and embedding therapy(9.2%) were difficult to perform during treatment because of cost. In the case of a later revision, respondents most thought it is necessary to update evidence and adjust recommendation ratings. A majority of all respondents said they would like to know about the revised guideline through the Internet. In the expected revision effect, the first order was 'presentation of standardized treatment method', the second was 'establishing the basis of Korean medicine treatment', and the third was 'strengthening the status of Korean medicine as therapeutic medicine'. Many respondents wished to add exercise therapy. In order to increase the utilization rate of the guideline, many respondents thought it should be included in textbooks and 90.6% of respondents answered that they would use more than 50% of the revised guideline. Conclusion: It is necessary to update evidence and adjust recommendation ratings and to promote KMCGP. At the same time treatment methods should be taught to K.M.D

• Journal of Information Technology Services
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• v.21 no.2
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• pp.109-126
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• 2022

The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.59-68
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• 2022

A recommender system covers users, searches the items or services which users will like, and let users purchase them. Because recommendations from a recommender system are predictions of users' preferences for the items which they do not purchase yet, it is rarely possible to be drawn a perfect answer. An evaluation has been conducted to determine whether a prediction is right or not. However, it can be lower user's satisfaction if a recommender system focuses on only the preferences, that is caused by a 'filter bubble effect'. The filter bubble effect is an algorithmic bias that skews or limits the information an individual user sees on the recommended list. It is the reason why multiple metrics are required to evaluate recommender systems, and a diversity metrics is mainly used for it. In this paper, we compare three different methods for enhancing diversity for personalized recommendation - bin packing, weighted random choice, greedy re-ranking - with a practical e-commerce data acquired from a fashion shopping mall. Besides, we present the difference between experimental results and F1 scores.