• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.251-262
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• 2015

This study checks the conceptual definition of domestic book curation which is still in the beginning stage, the necessity of developing service and business, domestic and overseas case of relevant service. Further, the problem of book recommendation service and the difficulty anticipated in the embodiment of service are investigated together and the business model as new IT service is suggested to supplement them. Specifically, the collection of book information and customer information (interest and purchase pattern) and the procedure of mining the collected information and the process of embodying visualization was presented in the sector of service in the first place. Then, the technical transfer of developed solution and the construction cost and the method to impose commission over contents sales are presented in the sector of business. Diverse social and economic effects are expected to realize by developing and utilizing such services, namely, promoting the distribution of excellent book which were kept in dead storage so far due to lack of marketing support, recommendation readers the proper books which are convenient and necessary.

• Journal of KIISE
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• v.44 no.9
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• pp.908-917
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• 2017

As web service technologies are widely used, there have been many efforts to develop approaches for recommending appropriate web services to users in complex and dynamic service environments. In addition, for the effective development of service mashups, service recommender systems that are specialized for service composition have been developed. However, existing service recommender systems for service mashups are not effective at recommending services in a personalized manner that reflect developers' preferences. To deal with this issue, we propose an approach that recommends services based on the similarities between mashup developers who have developed similar service mashups. The proposed approach is then evaluated by using the mashup data retrieved from ProgrammableWeb. The evaluation results clearly show that the proposed approach is an effective way of improving service recommendations compared to the traditional user-based collaborative filtering algorithm.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.756-761
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• 2015

With the emergence of the Internet-of-Things (IoT) paradigm, there is a growing demand for personalized services using IoT contexts acquired from heterogeneous IoT devices. However, due to the mismatch between IoT context exchange schemes of context-aware services and IoT devices, IoT applications can acquire IoT contexts only from IoT devices that support the same IoT context exchange schemes. In this paper, we propose dynamic methods to mediate those mismatches on the IoT context exchange schemes. With the proposed mediation methods, context-aware services can collect IoT contexts from heterogeneous IoT devices without considering their IoT context exchange schemes.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.3
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• pp.129-137
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• 2015

Various personalized services are provided based on user context these days, and IoT(Internet of Things) devices provides effective ways to collect user context. For example, user's activity such as walking steps, calories, and sleeping hours can be collected using smart activity tracker. Smart scale can sense change of user's weight or body fat percentage. However, these services are independent to each other and not easy to make them collaborate. Many standard bodies are working on the documents for this issue, but due to diversity of IoT use case scenarios, it seems that multiple IoT technologies co-exist for the time being. This paper propose a framework to collaborate heterogeneous IoT services. The proposed framework provides methods to build application for heterogeneous IoT devices and user context management in more intuitive way using HTTP. To improve compatibility and usability, gathered user contexts are based on MPEG-UD. Implementation of framework and service with real-world devices are also presented.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.2
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• pp.55-61
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• 2012

Due to customers' constantly increasing demand for personalized products and services, manufacturing enterprises integrate tangible physical products and intangible services to provide more diverse physical products and services. Many researchers are concerned with Product-Service Systems (PSS) modeling, but we have wondered on the interactions between the product and the service in a PSS. To find relationships between the two in a PSS, the survey results on utilization of smart phone are analysed and compared based on the PSS design framework to identify design factors for PSS.

• Journal of Information Technology Services
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• v.21 no.6
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• pp.21-37
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• 2022

Due to the development of technology and medical care following the 4th industrial revolution, the medical paradigm is shifting towards patient-centered medical services. Based on the development of smart home technology, the residential environment is changing into a residential space that cares for and heals the lifestyles and the healthcare of families. As lifestyle changes, the concept of supporting smart home care based on the residential environment is making it possible to build a smart home IoT service design with enhanced accessibility and convenience for medical appointments and well-being lifestyle care. This paper is a study on user-centered health care smart home IoT service design suitable for family members based on the health care, beauty care, exercise care, and customized diet care beyond the conventional concept of health care monitoring. Based on the analysis, this paper proposes a personal care coordinate smart home service design in a human-centered wellness clinic care smart home service design environment. Human-centered wellness clinic smart home IoT service design is meaningful in presenting a vision for research on smart home service design that links hospital-linked and care-linked service industries, which should be considered from the smart home construction planning stage.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.31-33
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• 2022

Research information services such as ScienceON, RISS, and DBpia used by researchers during the R&D process mainly consist of a user interface in the form of a portal and search functions that respond to demands. However, the importance of open collaboration that freely shares and utilizes the research process and results and provides various tools and services necessary for collaborative research is gradually increasing, so there are also limitations to these service roles. Thus, we design WithON, which provides collaborative research environments of a preemptive response method, focusing on a personalized user interface to support the entire R&D cycle. We expect WithON would fundamentally change the existing research information services into collaborative research-centric ones.

• Journal of Next-generation Convergence Information Services Technology
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• v.7 no.2
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• pp.127-140
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• 2018

The research on the health promotion effect of the elderly through the health care service has been going on for a long time, but there is insufficient research to grasp the needs of elderly people in order to effectively provide health care services. In order to solve these problems, this study suggested the direction of health care service for elderly people by analyzing regional characteristics and demand among rural areas. To this end, the direction of improvement of customized healthcare service model was suggested through the analysis of the health - related program utilization status, health management method, health care service type, and contents demand of the elderly by urban area and rural area.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.115-130
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• 2011

Many online stores bring features that can build trust in their customers. More so, the number of products or content services on online stores has been increasing rapidly. Hence, personalization on online stores is considered to be an important technology to companies and customers. Recommender systems that provide favorable products and customer product reviews to users are the most commonly used features in this purpose. There are many studies to that investigated the relationship between social presence as an antecedent of trust and provision of recommender systems or customer product reviews. Many online stores have made efforts to increase perceived social presence of their customers through customer reviews, recommender systems, and analyzing associations among products. Primarily because social presence can increase customer trust or reuse intention for online stores. However, there were few studies that investigated the interactions between recommendation type, product type and provision of customer product reviews on social presence. Therefore, one of the purposes of this study is to identify the effects of personalized recommender systems and compare the role of customer reviews with product types. This study performed an experiment to see these interactions. Experimental web pages were developed with $2{\times}2$ factorial setting based on how to provide social presence to users with customer reviews and two product types such as hedonic and utilitarian. The hedonic type was a ringtone chosen from Nate.com while the utilitarian was a TOEIC study aid book selected from Yes24.com. To conduct the experiment, web based experiments were conducted for the participants who have been shopping on the online stores. Participants were a total of 240 and 30% of the participants had the chance of getting the presents. We found out that social presence increased for hedonic products when personalized recommendations were given compared to non.personalized recommendations. Although providing customer reviews for two product types did not significantly increase social presence, provision of customer product reviews for hedonic (ringtone) increased perceived social presence. Otherwise, provision of customer product reviews could not increase social presence when the systems recommend utilitarian products (TOEIC study.aid books). Therefore, it appears that the effects of increasing perceived social presence with customer reviews have a difference for product types. In short, the role of customer reviews could be different based on which product types were considered by customers when they are making a decision related to purchasing on the online stores. Additionally, there were no differences for increasing perceived social presence when providing customer reviews. Our participants might have focused on how recommendations had been provided and what products were recommended because our developed systems were providing recommendations after participants rating their preferences. Thus, the effects of customer reviews could appear more clearly if our participants had actual purchase opportunity for the recommendations. Personalized recommender systems can increase social presence of customers more than nonpersonalized recommender systems by using user preference. Online stores could find out how they can increase perceived social presence and satisfaction of their customers when customers want to find the proper products with recommender systems and customer reviews. In addition, the role of customer reviews of the personalized recommendations can be different based on types of the recommended products. Even if this study conducted two product types such as hedonic and utilitarian, the results revealed that customer reviews for hedonic increased social presence of customers more than customer reviews for utilitarian. Thus, online stores need to consider the role of providing customer reviews with highly personalized information based on their product types when they develop the personalized recommender systems.