• Korea Journal of Hospital Management
• /
• v.22 no.3
• /
• pp.31-45
• /
• 2017

The research is to examine medical service quality factors affecting choice of hospital(revisiting intention, and recommendation) in large general hospitals based on the SERVQUAL model. The study have surveyed 400 respondents in Gangbuk-gu not having any tertiary hospital. The main results of the analyses indicate: 1) 'assurance' and 'empathy' of medical service are basically, positively affect revisiting intention and recommendation; 2) 'empathy' is the most important factor affecting revisiting intention; and 3) 'tangibility' significantly affects recommendation of general hospitals to other people. The study suggests that it is necessary to pay more attention on 'empathy' among SERVQUAL factors to increase satisfaction of patients and to find better ways of improving medical service quality.

• Journal of Information Processing Systems
• /
• v.17 no.2
• /
• pp.352-368
• /
• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.2
• /
• pp.437-442
• /
• 2021

In this study, a recommendation algorithm was implemented by learning a deep learning-based classification model for consumer data. For this purpose, a meaningful result is presented as a result of learning using ResNet50, which is commonly used in classification tasks by converting user data into images.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.886-887
• /
• 2023

This paper is about the development of kiosks that provide four types of service. Simple UI and educational videos solve the complexity of existing kiosks and provide an intuitive and convenient screen to users. In addition, the AR function, which is a three-dimensional form, shows directions and store representative images. After storing user information in the DB, a learning model is generated using user-based KNN collaborative filtering to provide a recommendation menu. As a result, it is possible to increase user convenience through kiosks using metaverse and AI recommendation services. It is also expected to solve digital alienation of social classes who have difficulty using kiosks.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.6 no.3
• /
• pp.19-29
• /
• 2010

A marketing model is changed from a customer acquisition to customer retention and it is being moved to a way that enhances the quality of customer interaction to add value to our customers. Such personalization is emerging from this background. The Web site is accelerate the adoption of a personalization, and in contrast to the rapid growth of data, quantitative analytical experience is required. For the automated analysis of large amounts of data and the results must be passed in real time of personalization has been interested in technical problems. A recommendation algorithm is an algorithm for the implementation of personalization, which predict whether the customer preferences and purchasing using the database with new customers interested or likely to purchase. As recommended number of users increases, the algorithm increases recommendation time is the problem. In this paper, to solve this problem, a recommendation system based on clustering and dimensionality reduction is proposed. First, clusters customers with such an orientation, then shrink the dimensions of the relationship between customers to low dimensional space. Because finding neighbors for recommendations is performed at low dimensional space, the computation time is greatly reduced.

• Nutrition Research and Practice
• /
• v.4 no.1
• /
• pp.75-81
• /
• 2010

This study explored the conceptual framework of dieticians' intentions to recommend functional food and the mediating role of consumption frequency. A web-based survey was designed using a self-administered questionnaire. A sample of Korean dieticians (N=233) responded to the questionnaire that included response efficacy, risk perception, consumption frequency, and recommendation intention for functional foods. A structural equation model was constructed to analyze the data. We found that response efficacy was positively related to frequency of consumption of functional foods and to recommendation intention. Consumption frequency also positively influenced recommendation intention. Risk perception had no direct influence on recommendation intention; however, the relationship was mediated completely by consumption frequency. Dieticians' consumption frequency and response efficacy were the crucial factors in recommending functional foods. Dieticians may perceive risks arising from the use of functional foods in general, but the perceived risks do not affect ratings describing dieticians' intentions to recommend them. The results also indicated that when dieticians more frequently consume functional foods, the expression of an intention to recommend functional foods may be controlled by the salience of past behaviors rather than by attitudes.

• Journal of Internet Computing and Services
• /
• v.10 no.5
• /
• pp.145-152
• /
• 2009

Applications such as study guides and adaptive tutoring must rely on a fine grained student model to tailor their interaction with the user. They are useful for Computer Adaptive Testing (CAT), for example, where the test items can be administered in order to maximize the information. I study how to design learner tailoring question process for recommendation. And this process can be applied the CAT and I use the formal language such as CSP in each process development for efficient process design. I use the item difficulty of item response theory for question recommendation process and learner can choice the difficulty step for learning change to control the difficulty of question in next learning. Finally, this method displayed the structural difference to compare between existent and this process.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.12 no.1
• /
• pp.265-273
• /
• 2012

It is essential to have Context-aware technology for personalization recommendation services and the appropriate representation and definition of Context information for context-aware. Ontology is possible to represent knowledge freely and knowledge can be extended by inferring. In addition, design of the ontology model is needed according to the purposes of utilization. This paper used context-aware technologies to implement a user personalization recommendation service. It also proposed the context through OWL modeling for user personalization recommendation service and used inference rules and inference engine for context reasoning.

• International Journal of Advanced Culture Technology
• /
• v.7 no.4
• /
• pp.321-326
• /
• 2019

As AI technology is recently introduced into various fields, it is being applied to the fashion field. This paper proposes a system for recommending cody clothes suitable for a user's selected clothes. The proposed system consists of user app, cody recommendation module, and server interworking of each module and managing database data. Cody recommendation system classifies clothing images into 80 categories composed of feature combinations, selects multiple representative reference images for each category, and selects 3 full body cordy images for each representative reference image. Cody images of the representative reference image were determined by analyzing the user's preference using Google survey app. The proposed algorithm classifies categories the clothing image selected by the user into a category, recognizes the most similar image among the classification category reference images, and transmits the linked cody images to the user's app. The proposed system uses the ResNet-50 model to categorize the input image and measures similarity using ORB and HOG features to select a reference image in the category. We test the proposed algorithm in the Android app, and the result shows that the recommended system runs well.

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.12
• /
• pp.461-468
• /
• 2018

Developers spend a significant amount of time exploring and trying to understand source code to find a source location to modify. To reduce such time, existing studies have recommended the source location using statistical language model techniques. However, in these techniques, the recommendation does not occur if input data does not exactly match with learned data. In this paper, we propose a code location recommendation method using Recurrent Neural Networks and interaction histories, which does not have the above problem of the existing techniques. Our method achieved an average precision of 91% and an average recall of 71%, thereby reducing time for searching and exploring code more than the existing recommendation techniques.