• The Journal of Bigdata
• /
• v.7 no.2
• /
• pp.173-193
• /
• 2022

With the revision of the Data 3 Act, the financial MyData industry was created newly. MyData services collect the financial customers' data scattered in various financial companies and provide personalized services such as personal financial product recommendation, personal expenditure advice, etc. Although MyData service started in 2022, but the use of the service has not been significantly activated. This study attempted to analyze the factors affecting the use of MyData services from the perspective of financial consumers through VAM, UTAUT2 model. The factors related to the perceived value and intention to use MyData services of financial consumers were verified using benefit and sacrifice variables. Personal Innovativeness was used as a moderating variable. As a result of this study, it was found that personal product recommendation service has an important influence on the use of MyData services, and personal innovativeness has an effect as a modulating variable. It can be said that it is meaningful as a preceding study in terms of timing because it studied the perceived value of consumers less than a year after the MyData service began. From the practical perspectives, it was possible to show the change direction and marketing points of the MyData service. In practice, it was possible to confirm the direction of the service and the marketing point.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.2
• /
• pp.339-346
• /
• 2021

In this paper, we implemented an interface that provides a better food ordering mechanism and enables real-time selection of recipe ingredient ratios for customized food orders from global customers. Providing appropriate food to global customers by arranging a selection of menu on the order system screen that shows the basic ratio of each recipe ingredient and provides a customized recipe ingredient composition ratio by configuring a recipe graph without a system for simply selecting and ordering food menus. By enabling interaction, it allows users to provide customized services through the ratio adjustment of various recipe ingredients in the food menu ordering device

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.8
• /
• pp.99-105
• /
• 2010

Because in the amusement park, a number of people use various vehicles facilities complicated arraigned, it needs an effective way to search optimal path to reduce errors in touring a park. Particularly, when choosing a facility, searching a waiting time-based path as well as shortest path is important. This paper presents a path recommendation system which minimizes total park tour time based on tour distance and waiting time through GPS and wireless internet. This system can also recommend customized tour path based on the characteristics of user members as well as a simple shortest path.

• Journal of Broadcast Engineering
• /
• v.16 no.2
• /
• pp.237-246
• /
• 2011

In order to produce the variety of UCC, the recommendation service is required which considers the copyright of UCC producer discriminated from one for UCC consumers and the purpose of its production. The recommender system designed in this thesis enables UCC which is much similar to one UCC producer utilizes to be used with custom-made when recommending and producing based on UCC view history and production list, etc. of its producer. The recommender system is largely divided into filtering based on the preferred tag, UCC filtering used when producing the preferred UCC and creating process of recommended UCC using the Pearson formula. The recommender system in this thesis requires the data which were used when producing UCC. For that, we added the reference factor so that the data of UCC which were utilized when producing UCC into the existing metadata can be recorded. If the recommender system suggested in this thesis is used, the more effective and convenient UCC recommendation services with custom-made for producers can be provided.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.11a
• /
• pp.313-314
• /
• 2009

추천 시스템은 고객의 탐색 시간과 노력을 줄여주기 위한 시스템으로서 고객의 만족도를 제고시키기 위한 시스템에 대한 많은 연구들이 진행되고 있다. 본 논문은 사용자의 프로파일과 음식 주문 내용을 기반으로 개인의 선호도를 분석하여 음식을 추천할 뿐 아니라 새로운 음식에 대한 정보를 제공하기 위해 데이터 마이닝 기법 중 연관규칙을 사용하여 시스템의 유연성을 높인 음식 추천 에이전트를 제안하고 구축한다. 본 시스템은 시간이 지남에 따라 사용자의 만족도가 상승하는 것을 알 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1599-1607
• /
• 2022

The data mainly used for the model are as follows: the personal information, the information of insurance product, etc. With the data, we suggest three types of models: content-based filtering model, collaborative filtering model and classification models-based model. The content-based filtering model finds the cosine of the angle between the users and items, and recommends items based on the cosine similarity; however, before finding the cosine similarity, we divide into several groups by their features. Segmentation is executed by K-means clustering algorithm and manually operated algorithm. The collaborative filtering model uses interactions that users have with items. The classification models-based model uses decision tree and random forest classifier to recommend items. According to the results of the research, the contents-based filtering model provides the best result. Since the model recommends the item based on the demographic and user features, it indicates that demographic and user features are keys to offer more appropriate items.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.95-117
• /
• 2022

With the emergence of various media types due to the development of Internet technology, advertisers have difficulty choosing media suitable for corporate advertising strategies. There are challenging to effectively reflect consumer contextual information when advertising media is selected based on traditional marketing strategies. Thus, a recommender system is needed to analyze consumers' past data and provide advertisers with personalized media based on the information consumers needs. Since the traditional recommender system provides recommendation services based on quantitative preference information, there is difficult to reflect various contextual information. This study proposes a methodology that uses deep learning to recommend personalized media to advertisers using consumer contextual information such as consumers' media viewing time, residence area, age, and gender. This study builds a recommender system using media & consumer research data provided by the Korea Broadcasting Advertising Promotion Corporation. Additionally, we evaluate the recommendation performance compared with several benchmark models. As a result of the experiment, we confirmed that the recommendation model reflecting the consumer's contextual information showed higher accuracy than the benchmark model. We expect to contribute to helping advertisers make effective decisions when selecting customized media based on various contextual information of consumers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.316-318
• /
• 2019

Recently, healthy life has become an issue in an aging society, and the number of people who have been interested in continuous health care for better life is increasing. In this paper, we implemented a personalized recommendation systm to provide convenient healthcare management for user. The PHR (Personal Health Record) of user could be stored in the server along with health related information such as lifestyle, disease, and physical condition. The users could be classified into similar clusters according to the PHR profile in order to provide healthcare contents to the users who had similar PHR profile. K-Means clustering was applied to generate clusters based on PHR profile and ACDT(Ant Colony Decision Tree) algorithm was used to provide personalised recommendation of health information stored in knowledge base. The app system developed in this paper is useful for users to perform healthcare themselves by providing information on serious diseases and lifestyle habits to be improved according to the clusters classified by PHR profile.

• The Journal of the Korea Contents Association
• /
• v.16 no.10
• /
• pp.435-442
• /
• 2016

Obesity extends the global economic burden and it causes that the failure of a reduction of physical activity, and diet management. In this work, nutritional information and personalized diet based on calorie supply system and is discriminatory utilized the obesity-related SNP information in order to recommend a personalized functional foods. This study performed a GWAS analysis for the excavation of a Korean-specific and obesity-related SNP, which utilizes genetic information were recommended by entering a personalized diet in accordance with the SNP genotype-phenotype information. In addition, we integrated Database with relation of nutrient for utilizing the USDA Food information and it was applied to recommend with Sickdanome. As a result, the obesity-related SNP information was confirmed in the sample which has the normal value BMI. In this study, we have recognized that the phenotype information related obesity, BMI is inconsistent with the SNP genotype information. This result is shown that it is necessary to provide the personalized dietary SickdanOme recommendation based on the both pheotype-genotype information.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.04a
• /
• pp.741-744
• /
• 2015

최근 소셜 미디어를 이용한 대량의 데이터로부터 사용자의 의사결정을 지원하기위한 맞춤형 데이터 추천 서비스가 관심을 받고 있으며 사용자의 선호도에 근접한 데이터 추천기법으로 스카이라인 질의가 연구되어왔다. 그러나 기존의 스카이라인 질의는 데이터의 정적속성(위도, 경도, 가격 등)만을 기준으로 모든 사용자에게 동일한 데이터를 반환하기 때문에 맞춤형 데이터를 추천하기 어렵다. 본 논문에서는 사용자의 기호에 대한 정밀도를 높이기 위해 정적속성에서 동적속성(계산속성)을 유도하는 분기한정 동적 스카이라인 질의 기법(Branch and Bound Dynamic Skyline, BBDS)을 구현하였다. 시뮬레이션에서는 대규모 데이터 및 다양한 분포에 따른 실험을 수행한 결과 BBDS가 기존 기법에 비해 데이터 탐색과 추천에 있어서 향상된 성능을 나타내는 것으로 평가되었다.