• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.341-342
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• 2021

본 논문에서는 운동 관련 빅데이터를 적용한 인공지능을 활용하여 개개인에게 알맞은 운동 루틴을 추천해 주는 비대면 방식 PT를 제안한다. 이 정책은 '건강한 사람이 앱을 만나 더 건강해진다.'는 모토를 중심으로, 홈 트레이닝을 하고 싶지만 운동방법을 모르는 사람들로 하여금 자신에게 맞추어진 루틴 구성을 통해 운동 수행능력의 효율성을 높이고, 잘못된 자세로 인한 부상 등을 최소화한다. 또한 이 정책은 기존의 일일이 사용자가 입력해야 했던 시스템들에서 머신러닝을 통한 AI 알고리즘을 통한 추천을 통해 비대면 방식의 수동적인 운동 방식에서 AI가 트레이너 역할을 해주는 방식으로 사용자와 상호작용하고, 정확한 운동 목표를 추천함으로써 운동 지속성과 동기성을 부여한다. 본 논문에서는 프로토타입을 통해 제안하는 AI를 적용한 맞춤 헬스 정책이 기존의 헬스 앱 업계에서 시장성을 보일 수 있다는 가능성에 의의를 둔다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.413-414
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• 2023

IT 기술의 발달로 E-Commerce 분야는 실시간으로 발생되는 데이터양이 증가하고 있으며, 발생된 데이터는 개인화 맞춤 서비스에 많이 활용되고 있다. 그러나 신생 E-commerce 기업은 신규 상품 및 기존 상품에 대한 정보와 고객 간의 상호 작용 데이터가 존재하지 않아 콜드 스타트 문제가 발생한다. 이에 본 논문에서는 E-commerce 환경에서 실시간 사용자 구매패턴 분석을 통한 사용자 상품 추천 시스템을 제안한다. 제안하는 시스템은 Kafka와 Spark를 사용해 실시간 스트림을 데이터를 처리한다. 주요 기능은 ALS 알고리즘과, FP-Growth 알고리즘을 적용해 콜트 스타트 문제를 해결하며, 사용자 구매 패턴 분석을 통한 분석 결과에 맞는 상품을 사용자에게 추천한다.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.292-302
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• 2007

Thousands of studies on predicting information and products that are suitable for customers' preference have been actively proceeding. In massive information, unnecessary information should be removed to satisfy customers' needs. This Information filtering has been proceeding with several methods such as content-based and collaborative filtering etc. These conventional filtering methods have scarcity and scalability problems. Thus, this paper proposes a recommendation system using BPN to solve them. Data obtained by survey questionnaire are used as training data of neural network. The recommendation system using neural network is expected to recommend suitable products because it creates optimal network. Finally, the prototype for recommendation system based on neural network is proposed to collect data and recommend appropriate methods through survey questionnaire. As a result, this research improved the problems of conventional information filtering.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.203-214
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• 2020

Nowadays, smart speakers are increasingly personalized and serve as recommendation agents for user. The aim of this study is find out effects of 'Explanation facilities' on transparency, perceived trust, user satisfaction, behavioral intentions of users to reuse, privacy risk, and quality of recommendation in the context of an interact with smart speaker's conversational agents. And we also use measurement for level of privacy concerns to see individuals's level of privacy concerns affected the assessment. The result of this study as follow; First, all measurement variable are significantly related to 'Explanation facilities' Second, perceived trust, privacy risk are significantly related to individual's level of privacy concern. This study found that 'Explanation facilities' could be applied in context of smart speaker and possibility of cognitive dissonance according to the level of privacy concerns.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2789-2791
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• 2001

데이터 마이닝을 통해 우리는 숨겨진 지식, 예상되지 않았던 경향 그리고 새로운 법칙들을 방대한 데이터에서 이끌어내고자 한다. 본 논문에서 우리는 사용자의 구매 패턴을 발견하여 사용자가 원하는 상품을 미리 예측하여 추천하는 알고리즘을 소개하고자 한다. 제안하고 있는 item dependency map은 구매된 상품간의 관계를 수식화 하여 행렬의 형태로 표현한 것이다. Item dependency map의 값은 사용자가 A라는 상품을 구매한 후 B상품을 살 확률이다. 이런 정보를 가지고 있는 item dependency map은 홉필드 네트윅(Hopfield network)에서 연상을 위한 패턴 값으로 적용된다. 홉필드 네트웍은 각 노드사이의 연결가중치에 기억하고자 하는 것들을 연상시킨 뒤 어떤 입력을 통해서 전체 네트워크가 어떤 평형상태에 도달하는 방식으로 작동되는 신경망 중의 하나이다. 홉필드 네트웍의 특징 중의 하나는 부분 정보로부터 전체 정보를 추출할 수 있는 것이다. 이러한 특징을 가지고 사용자들의 일반적인 구매패턴을 일부 정보만 가지고 예측할 수 있다. Item dependency map은 홉필드 네트윅에서 사용자들의 그룹별 패턴을 학습하는데 사용된다. 따라서 item dependency map이 얼마나 사용자 구매패턴에 대한 정보를 가지고 있는지에 따라 그 결과가 결정되는 것이다. 본 논문은 정확한 item dependency map을 계산해 내는 알고리즘을 주로 논의하겠다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.11-17
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• 2010

In this paper, we propose a recommendation system for supporting self-directed programming language education. The system is a recommendation system using collaborative filtering based on learners' level and stage. In this study, we design a recommendation system which uses collaborative filtering based on learners' profile of their level and correlation profile between learning topics in order to increase self-directed learning effects when students plan their learning process in e-learning environment. This system provides a way for solving a difficult problem, that is providing programming problems based on problem solving ability, in the programming language education system. As a result, it will contribute to improve the quality of education by providing appropriate programming problems in learner"s level and e-learning environment based on teaching and learning method to encourage self-directed learning.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.615-620
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• 2013

With the development of smartphones, the number of applications for smartphone increases sharply. As a result, users need to try several times to find their favorite apps. In order to solve this problem, we propose a recommendation system to provide an appropriate app list based on the user's log information including time stamp, location, application list, and so on. The proposed approach learns three recommendation models including Naive-Bayesian model, SVM model, and Most-Frequent Usage model using temporal and spatial attributes. In order to figure out the best model, we compared the performance of these models with variant features, and suggest an hybrid method to improve the performance of single models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.155-164
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• 2009

Digital Content Marketplace based on Interactive Media is defmed as the marketplace for content service between contents supplier and consumer through iDTV environment. This Marketplace is increasing interest to u-Life service with Digital Environment. To Interactive Media, it can contribute to enhance its effectiveness by developing various contents and service model in the initial phase of broadcasting-communication convergence. This study designed metadata using Digital Content marketplace based on Interactive Media. Specially the matadata designing include recommendation-tag for supply supplementary content. It can support self-directed action. Through basic metadata with weight value, it is designed to support supplementary content customer to want on the marketplace. Recommendation-System can be built by many method and to recommend the service content including explicit properties using collaborative filtering method can solve limitations in existing content recommendation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.281-283
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• 2021

The representative companies mentioned in the recommendation service in the domestic OTT(Over-the-top media service) market are YouTube and Netflix. YouTube, through various methods, started personalized recommendations in earnest by introducing an algorithm to machine learning that records and uses users' viewing time from 2016. Netflix categorizes users by collecting information such as the user's selected video, viewing time zone, and video viewing device, and groups people with similar viewing patterns into the same group. It records and uses the information collected from the user and the tag information attached to the video. In this paper, we propose a method to improve video media recommendation by automatically generating metadata of video media that was written by hand.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.40-50
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• 2003

Ontology is the artifacts for representing the truth or the states of objects by defining objects and their relations. In this paper, we propose an agent that classifies Web documents and provides personalized information towards user`s information needs using ontology. the agent uses ontology in which semantic relations on Web documents are represented in ta hierarchical form to classify Web documents. In this paper, ontology consists of concepts, features(describing concepts), relations(among concepts) and constraints(among elements in a feature). The agent can capture user's information needs efficiently by using ontology and assist Web navigation by using users profiles and the results of identification of semantic relations in Web documents. Also, the agent obtains Web documents by a look-ahead search and represents them as concepts, therefore users can understand them easily by receiving recommendations expressed in the form of high-level concepts.