• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.309-310
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• 2019

최근 들어 빅데이터 기반의 추천 방식과 개인화 시스템을 활용하여 맞춤형 콘텐츠를 추천해주는 서비스가 주목받고 있다. 이는 단순히 OTT 서비스뿐만 아니라 상품추천이나 음악 추천, 친구 추천, 뉴스 추천 등 여러 분야에서도 널리 사용 중이다. 본 연구는 OTT 서비스의 맞춤형 콘텐츠를 지속해서 이용하는 경우 정보 탐색 과정의 사용 경험과 이용만족도에 대해 알아보고자 시작되었다. OTT 서비스 중 사용자가 가장 많고 콘텐츠 추천 기능이 강점인 넷플릭스와 왓챠플레이를 중심으로 사용자 인터뷰를 진행하여 사용자들의 추천 기능 이용 패턴을 파악하고 그 과정에서의 특이사항이나 어려움을 파악하려 하였다. 이를 바탕으로 콘텐츠 추천 및 탐색 과정의 UX를 개선할 수 있는 방안을 제시하고자 하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.609-616
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• 2016

This paper is proposed a customized market analysis method using SVM and collaborative filtering. The proposed customized market analysis scheme is consists of DC(Data Classification) module, ICF(Improved Collaborative Filtering) module, and CMA(Customized Market Analysis) module. DC module classifies the characteristics of on-line and off-line shopping mall and traditional markets into price, quality, and quantity using SVM. ICF module calculates the similarity by adding age weight and job weight, and generates network using the similarity of purchased item each users, and makes a recommendation list of neighbor nodes. And CMA module provides the result of customized market analysis using the data classification result of DC module and the recommendation list of ICF module. As a result of comparing the proposed customized recommendation list with the existing user based recommendation list, the case of recommendation list using the existing collaborative filtering scheme, precision is 0.53, recall is 0.56, and F-measure is 0.57. But the case of proposed customized recommendation list, precision is 0.78, recall is 0.85, and F-measure is 0.81. That is, the proposed customized recommendation list shows more precision.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.305-308
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• 2011

본 논문은 고객관리 정보를 관리하기 위해 휴대하기 간편한 스마트폰으로 고객의 정보를 입력 및 관리하고 상품에 대한 간략한 설명을 스마트폰으로 활용하고자 본 연구를 수행하였으며, 테스트를 수행하기 위해 안드로이드 기반 보험고객관리 앱을 구현하여 활용성을 검증하였다. 보험설계자의 개인적 특성에 맞게 필드를 구성하여 맞춤형으로 정보를 등록하여 놓고 검색을 통해 고객의 기념일, 특성 등을 확인하여 미팅이나, 보험상품의 권유에 활용할 수 있다. 본 고객관리 앱은 모든 CRM(Customer Relationship Management)분야에 활용 가능하며 필드를 정비하여 계약자의 정보가 보험사의 가입자 정보와 연동되도록 확장하면 업무의 절차가 간소화 될 수 있을 것이다.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.12
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• pp.1710-1720
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• 2007

Environmental changes, including intensive international competition, unpredictable consumer demand, and market trends of variety and short product life cycles, have compelled the U. S. textiles and apparel industry to focus increasingly on the consumer as a way to meet these challenges. The industry began expanding into mass customization that used information technology, flexible processes, and organizational structures to deliver a wide range of products and services that met specific needs of individual customers but on a mass scale. This paper presents cases of leading-edge technology application on customization and breakthrough concepts in personalization, with a view to raising the level of debate on these issues to its highest level.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.183-188
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• 2021

Collaborative filtering based recommender systems are currently indispensable function of commercial systems in various fields, being a useful service by providing customized products that users will prefer. However, there is a high possibility that the prediction of preferrable products is inaccurate, when the user's rating data are insufficient. In order to overcome this drawback, this study suggests a stepwise method for prediction of product ratings. If the application conditions of the prediction method corresponding to each step are not satisfied, the method of the next step is applied. To evaluate the performance of the proposed method, experiments using a public dataset are conducted. As a result, our method significantly improves prediction and precision performance of collaborative filtering systems employing various conventional similarity measures and outperforms performance of the previous methods for solving rating data sparsity.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.19-24
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• 2020

Recently, due to the rapid increase of pets, there is a need for an integrated pet-related personalized product recommendation service such as feed recommendation using a health status check of pets and various collected data. This paper implements a product recommendation system that can perform various personalized services such as collection, pre-processing, analysis, and management of pet-related data using big data. First, the sensor information worn by pets, customer purchase patterns, and SNS information are collected and stored in a database, and a platform capable of customized personalized recommendation services such as feed production and pet health management is implemented using statistical analysis. The platform can provide information to customers by outputting similarity product information about the product to be analyzed and information, and finally outputting the result of recommendation analysis.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.395-401
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• 2014

As social network service becomes common, the consumers use many discount coupons with which they can purchase goods via social commerce. Although, the quantities of coupons offered from social commerce are currently on the sharp increase, customized coupon service that reflects user preference is not offered. This paper proposes a coupon service method reflecting user's subjective inclination targeting food coupons to offer customized coupon service for social commerce. Towards this end, this paper conducts hierarchization of the factors that become standard in selecting coupons including food types, food prices, discount rates and the number of buyers. And then, this study classifies, extracts and offers the coupons using Fuzzy-AHP, a decision making support method that reflects subjective inclination. From the user satisfaction results on the extracted coupons, the users are generally satisfied: very satisfactory with 45%, satisfactory with 33% and fair with 22%, and there was no experiment participant, who was dissatisfied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.635-638
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• 2018

The Cost for introducing smart factory will decrease and the service type will change from a large scale to small quantity manufacturing, when 3D printing technologies have actively applied and smart factory related technologies have more stably developed. If customers have to provide private information, the availability of developed technology may cause slow progress. We propose a new protocol for custom manufacturing service of smart factory. The proposed approach is designed for smart contract based IoT convergence network. We analyzed the requirements of the proposed approach which provides anonymity, privacy, fairness, and non-repudiation. We compared it with closely related studies to show originality and differences.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.215-222
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• 2019

Advances in technologies about 3D (three-dimensional) printing and smart factory related issues will have the effect of reducing the cost of building a smart factory and making various types of service available. Manufacturers and service providers of small assets work with outside experts to provide small amounts of customized ordering services. If customers have to disclose their private information to subscribe to a new service, they may be reluctant to use it and the availability of developed technology may cause slow progress. We propose a new protocol for customized order service for smart factory. The proposed approach is designed to meet requirements of security and based on smart contract in IoT convergence network. We analyzed the requirements of the proposed approach which provided anonymity, privacy, fairness, and non-repudiation. We compared it with closely related studies to show originality and differences.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.203-216
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• 2022

In the recent business environment, purchase patterns are changing around the influence of COVID-19 and the online market. This study analyzed cluster and correlation analysis based on purchase and product information. The cluster analysis of new methods was attempted by creating customer, product, and cross-bonding clusters. The cross-bonding cluster analysis was performed based on the results of each cluster analysis. As a result of the correlation analysis, it was analyzed that more association rules were derived from a cross-bonding cluster, and the overlap rate was less. The cross-bonding cluster was found to be highly efficient. The cross-bonding cluster is the most suitable model for recommending products according to customer needs. The cross-bonding cluster model can save time and provide useful information to consumers. It is expected to bring positive effects such as increasing sales for the company.