• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.139-144
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• 2015

Recently, to satisfy the customer's requirements for glasses such as rating of glasses or choosing, glasses industry is supporting various types and tools for making one's appearance good. Among various forms of online shopping with the development of online shopping and the rapid growth of IT technology, the intense competition also more deepen. The strategies for differentiated marketing and features for competitiveness of businesses in the online and offline shopping areas are required that our marketing target be settled the glasses marketing by analyzing. In this research, we study the strategy for implementing a site for glasses to customizing a person not to distinguish whether the customer is older or younger etc. For the purpose of implementing a site for glasses, first, we suggest a differentiated methodology through analysis of glasses industry service and features of existential representative glasses sites. Through the activation of the marketing strategies, marketing in a glasses market is expected to be competitive.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.835-840
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• 2008

This paper proposes an enhanced recommendation technique for personalized e-commerce portal analyzing various attitudes of customer. The attitudes are classifies into three types such as "purchasing product", "adding product to shopping cart", and "viewing the product information". We implicitly track customer attitude to estimate the rating of products for recommending products. We classified user groups which have similar preference for each item using implicit user behavior. The preference similarity is estimated using the Cross Correlation Coefficient. Our recommendation technique shows a high degree of accuracy as we use age and gender to group the customers with similar preference. In the experimental section, we show that our method can provide better performance than other traditional recommender system in terms of accuracy.

• Health Policy and Management
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• v.18 no.3
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• pp.1-17
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• 2008

The measurement and management of patient satisfaction has become one of the key issues in the last two decades. Hospitals must thoroughly understand the needs of their customers and design products and health services that meet and exceed their expectations. The importance-performance analysis(IPA) is a widely used analytical technique that yields strategies for managing customer satisfaction in a variety of applications. IP A is a two-dimensional grid based on customer-perceived importance of quality attributes and attribute performance. Depending on the interplay of these two dimensions, four strategies can be derived. The aim of this study is to develop the management strategies for improving patient satisfaction in university hospitals using the I-P analysis. The attributes on inpatient service quality in 4 university hospitals was investigated using the Martilla and James(l977)' s a mean adjusted I-P grid where the axes of the grid cross at the average rating point of all items. The patient satisfaction questionnaires were completed by 600 hospital inpatients. The main statistical methods are path analysis and IPA with SPSS 12.0 and AMOS 4.0 statistical softwares. The two attributes, physician and medical service, administrative staff kindness attributes position in first quadrant(Keep Up the Good domain). The nurse and nursing service attributes position in second quadrant(Possible Overkill domain). The two attributes, convenience of check-in service, facilities and physical environment position in third quadrant(Low Priority domain). Finally the quality of inpatient service(food etc.) attributes position in fourth quadrant(Concentrate Here domain). These findings show various implications on the development of strategies in university hospitals in the future. It was determined that quality of inpatient service(food etc.) need to concentrate more on investments. These investments include a taste, price, proper provision of food service and quick response of pain management. A low priority was given to investment in streamlining the check-in process of inpatient and hospital facilities and physical environment in the long run.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.227-235
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• 2010

This paper proposes the recommendation system which is advanced using RFM method and Association Rules in e-Commerce. Using a implicit method which is not used user's profile for rating, it is necessary for user to keep the RFM score and Association Rules about users and items based on the whole purchased data in order to recommend the items. This proposing system is possible to advance recommendation system using RFM method and Association Rules for cross-selling, and also this system can avoid the duplicated recommendation by the cross comparison with having recommended items before. And also, it's efficient for them to build the strategy for marketing and crm(customer relationship management). It can be improved and evaluated according to the criteria of logicality through the experiment with dataset collected in a cosmetic cyber shopping mall. Finally, it is able to realize the personalized recommendation system for one to one web marketing in e-Commerce.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.501-512
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• 2012

Social welfare maximization in a double-sided auction market is performed by implementing an aggregation-based particle swarm optimization (CAPSO) algorithm for optimal placement and sizing of one Static Synchronous Series Compensator (SSSC) device. Dallied simulation results (without/with line flow constraints and without/with SSSC) are generated to demonstrate the impact of SSSC on the congestion levels of the modified IEEE 14-bus test system. The proposed CAPSO algorithm employs conventional quadratic smooth and augmented quadratic nonsmooth generator cost curves with sine components to improve the accurate of the model by incorporating the valve loading effects. CAPSO also employs quadratic smooth consumer benefit functions. The proposed approach relies on particle swarm optimization to capture the near-optimal GenCos and DisCos, as well as the location and rating of SSSC while the Newton based load flow solution minimizes the mismatch equations. Simulation results of the proposed CAPSO algorithm are compared to solutions obtained by sequential quadratic programming (SQP) and a recently implemented Fuzzy based genetic algorithm (Fuzzy-GA). The main contributions are inclusion of customer benefit in the congestion management objective function, consideration of nonsmooth generator characteristics and the utilization of a coordinated aggregation-based PSO for locating/sizing of SSSC.

• Journal of Korean Critical Care Nursing
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• v.4 no.1
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• pp.65-73
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• 2011

Purpose: This study was to develop and evaluate stability and effects of an early exercise program for patients with open heart surgery. Methods: The subjects of this study were 30 patients who had either a coronary bypass surgery or a valvular heart surgery at a tertiary hospital in Seoul. The data was collected by observation and measurement from October 1, 2004 to November 15, 2004. Results: The early exercise program developed for this study consisted of range of motion exercise and walking. Intensity of walking was 1~3 METs and increased progressively to daily target distance. During exercise, the subjects were monitored heart rate, blood pressure and RPE (Rating of Perceived Exertion). The mean FIM (Functional Independent Measurements) score of subjects was significantly improved after the early exercise program. However, several complaints such as dizziness or pain were also reported. Most complaints were associated with chest tube and RPE. Conclusion: The early exercise program can help to recover patients' physical activities after surgery, and can be applied to most patients. Patients' RPE, dizziness and pain was possible limitations, therefore, active pain control and prevention of accidents for patients would be needed.

• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.83-88
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• 2016

In this research aimed to deduce internal marketing factors, purpose, and their practical application by analyzing preceding researches on internal marketing of Korean medical service organization and investigating the recent trend of its research. Subjects of research are ten preceding researches that have been published in KCI records for the last five years from 2011 to 2016. Summarize result of researches, first, internal factors that were most frequently used were internal communication, compensation system, and education and training, which were used by 8(.8). Second, occupations that had most interest in the internal marketing research appeared to be nursing(.9) and administration(.3). Third, the practical application of the internal marketing appeared to be job satisfaction(.8), followed by customer orientation(.6), and organizational commitment(.4). Suggestion do, necessary to develop subordinate factors regarding the realistic internal marketing, such as both-sided internal communication enhancement, education and training, compensation system differentiated by individuals and teams, fairness in performance rating, work environment improvement, delegation of authority, career development, shared organizational vision in order to maximize job satisfaction, job commitment, and organizational commitment of employees as internal customers, before establishing strategies to satisfy patients and guardians who are external customers.

• The Journal of Industrial Distribution & Business
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• v.10 no.4
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• pp.25-31
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• 2019

Purpose - A framework is suggested in this paper which will help crowdfunding platforms to match projects according to expectations of funders, leading to successful campaigns and thus increase the profitability of the crowdfunding platform. Research design, data, and methodology - The paper is theoretical and conceptual in nature which proposes a model for crowdfunding platforms to match expectations of crowds with project fundraisers. Results - Crowdfunding platforms are going through incremental innovations in order to match customer (funders and fundraisers) expectations. Leading crowdfunding platforms like Kickstart holds benchmark for other players in the market but the secret of success lies in matching quality projects with the appropriate funders. Crowdfunding platforms have to securitize the projects and allow only quality projects but also provide a wide range of options for funders. Thus, to manage this trade-off between quality and quantity of options, a framework is proposed. Conclusions - Crowdfunding platforms have to adopt a model which will help them in providing a perfect match between crowds and fundraisers. Each member of the crowd and every project will be assigned a category and rating based on the past records. Securitization of projects will help to entertain only demanded projects which will reduce the number of failing campaigns.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.33-39
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• 2020

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.