• 한국멀티미디어학회논문지
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• 제23권1호
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• pp.31-42
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• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.

• 경영정보학연구
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• 제24권3호
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• pp.69-91
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• 2022

짧은 동영상의 대표서비스인 틱톡의 사용자를 대상으로 프라이버시 계산 모델을 이용하여 틱톡의 추천서비스 유형(추천서비스 정도에 따라서 3단계로 구분함)에 대한 사용자의 반응(인지된 위험, 인지된 혜택, 지속적 사용의도)과 인지된 위험과 인지된 혜택이 지속적 사용의도에 미치는 영향을 검증하였다. 뿐만 아니라 지속적 사용의도에 영향을 조절하는 호기심의 역할이 있는지를 검증하였다. 연구 결과, 인지된 혜택(인지된 정보성)요인과 지속적 사용의도는 추천서비스 유형중에서도 고, 저, 중 추천 서비스정도 순으로 높게 나타났고, 인지된 혜택(인지된 오락성)과 인지된 위험(프라이버시 심각성, 프라이버시 침해 가능성)은 고, 중, 저의 추천 서비스정도 순으로 높게 나타났다. 인지된 혜택(인지된 정보성, 인지된 오락성)은 지속적 사용의도에 긍정적인 영향을 주었으나, 인지된 위험(프라이버시 심각성, 프라이버시 침해 가능성)은 지속적 사용의도에 부정적인 영향을 주는 것으로 확인되었다. 마지막으로 프라이버시 계산모델에서 호기심은 조절효과가 있다는 것을 확인하였다. 사용자들은 추천서비스의 프라이버시에 대한 우려와 서비스에 대한 혜택 모두를 인지하고 있으며, 추천서비스에 대해서 위험과 혜택이 모두 있지만 지속적으로 서비스를 이용할 것으로 나타났다. 더 많은 연구를 통해서 추천시스템의 긍정적인 효과와 부정적인 반응을 비교하여 사용자들의 프라이버시 허용정도에 대해서 좀 더 알게 되었을 때 추천서비스를 염려없이 사용할 수 있을 것으로 사료된다.

• 한국콘텐츠학회논문지
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• 제17권11호
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• pp.183-191
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• 2017

본 연구는 과학 학술정보 서비스 플랫폼 이용자의 정보 검색 편의성을 확보하고 적합한 정보의 획득에 소요되는 시간을 절약하기 위하여, 운영 중인 서비스 메뉴와 각 서비스 별 콘텐츠 정보를 제공하는 알고리즘 중 콘텐츠 추천 알고리즘을 최적화하고 그 결과를 비교평가 하는 것이다. 추천 정확도를 높이기 위해 이용자의 '전공' 항목을 기존 알고리즘에 추가하였으며, 기존 알고리즘과 최적화된 알고리즘을 통한 추천 결과의 성능평가를 수행하였다. 성능평가 결과 최적화된 알고리즘을 통해 이용자에게 제공되는 콘텐츠의 적합도가 21.2% 증가함을 파악하였다. 이용자에게 적합한 콘텐츠를 시스템에서 자동 도출하여 각 서비스 메뉴 별로 제공함으로써 정보 획득 시간을 단축하고, 연구정보로서 가치 있는 연구결과물의 생명주기를 연장할 수 있는 방안이라는 데 본 연구의 의의가 있다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권3호
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• pp.185-193
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• 2010

최근 유비쿼터스 시대의 도래와 함께 개인화된 서비스를 제공하기 위한 다양한 서비스 모델들이 제안되어 왔으며, 특히, 사용자에게 개인화된 서비스를 선응적으로 제공하기 위한 다양한 추천 서비스 기법들이 고안되었다. 그러나, 기존의 기법들은 수 많은 데이터를 여과 과정 없이 분석함으로써 추천의 효율성이 떨어지며, 한정된 상황 인지 정보만용 추천 요소로 고려하기 때문에 사용자에게 개인화된 서비스를 제공하기에 적합하지 않다. 본 논문에서는 유비쿼터스 환경에서 사용자의 현재 상황에 가장 적합한 서비스를 제공하는 적응형 추천 서비스 기법을 제안한다. 본 기법은 사용자의 선호도 예측을 위해 누적된 사용자와 장치 간의 상호작용 상황 정보들을 이용하며, 군집 및 협업 필터링 기법을 이용하여 사용자에게 현재 상황에 적응적인 서비스를 추천한다. 군집 기법을 통해 사용자의 현재 위치에 근접한 데이터만을 분석함으로써, 추천의 효율성을 높이며, 협업 필터링을 이용하여 누적된 정보들이 충분하지 않은 상황에서도 정확한 추천을 보장한다. 끝으로, 시뮬레이션을 통해 본 기법의 성능 및 신뢰성을 평가한다.

• 한국컴퓨터정보학회논문지
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• 제22권3호
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• pp.35-43
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• 2017

In IoT(Internet of Things) environment, users want to receive customized service by users' personal device such as smart watch and pendant. To fulfill this requirement, the mobile device should support a lot of functions. However, the miniaturization of mobile devices is another requirement and has limitation such as tiny display. limited I/O, and less powerful processors. To solve this limitation problem and provide customized service to users, this paper proposes a collaboration system for sharing various computing resources. The paper also proposes the method for reasoning and recommending suitable resources to compose the user-requested service in small device with limited power on expected time. For this goal, our system adopts MBTI(Myers-Briggs Type Indicator) to analyzes user's behavior pattern and recommends personalized resources based on the result of the analyzation. The evaluation in this paper shows that our approach not only reduces recommendation time but also increases user satisfaction with the result of recommendation.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.202-207
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• 2023

In this paper, we discuss the design and implementation of a recommendation platform actually built in the field. We survey deep learning-based recommendation models that are effective in reflecting individual user characteristics. The recently proposed RNN-based sequential recommendation models reflect individual user characteristics well. The recommendation platform we proposed has an architecture that can collect, store, and process big data from a company's commercial services. Our recommendation platform provides service providers with intuitive tools to evaluate and apply timely optimized recommendation models. In the model evaluation we performed, RNN-based sequential recommendation models showed high scores.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권11호
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• pp.5344-5356
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• 2018

Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users' activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user's activity are considered, helping users obtain ideal recommendation service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.

• 대한인간공학회지
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• 제27권4호
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• pp.85-94
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• 2008

Internet shopping has been getting more rousing due to extension of supply with PC(personal computer) and a rapid rise of use of internet. Some companies have been continually researching in how to serve individuals with each ordered information, which aimed at getting ordinary customers to induce to be loyal customers. For that, there is progress of a service of a web-recommendation which considers individual attribution. This study is suggested a method which is a service of the web-recommendation by access to sensibility ergonomics approach. Previous studies established that service had a weak point. It did not manage to realize new needs of customers. Proposed service of the web-recommendation has been designed, which preferentially propose goods included customer's sensibility to the customer who wants it. This study is expected that it will encourage a rise of products' purchasing power of customers, make an increase in a profit of both sellers and people who operate electric commercial and satisfaction of customers will go up in the same. Also, products accord with sensibility of customers will be recommended customers by the suggested service of the web-recommendation. In addition, there will be a decline of time-consuming about making a choice among some products.

• ETRI Journal
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• 제29권2호
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• pp.169-178
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• 2007

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context-aware active services based on context-aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context-aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context-aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.

• International Journal of Advanced Culture Technology
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• 제8권4호
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• pp.263-270
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• 2020

The growth of the mobile app markets has made it popular among people who recommend relevant information about restaurants. The recommendation service app based on AI Chatbot is that it can efficiently manage time and finances by making it easy for restaurant consumers to easily access the information they want anytime, anywhere. Eating out consumers use smartphone applications for finding restaurants, making reservations, and getting reviews and how to use them. In addition, social attention has recently been focused on the research of AI chatbot. The Chatbot is combined with the mobile messenger platform and enabling various services due to the text-type interactive service. It also helps users to find the services and data that they need information tersely. Applying this to restaurant recommendation services will increase the reliability of the information in providing personal information. In this paper, an artificial intelligence chatbot-based smartphone restaurant recommendation app using personalization information is proposed. The recommendation service app utilizes personalization information such as gender, age, interests, occupation, search records, visit records, wish lists, reviews, and real-time location information. Users can get recommendations for restaurants that fir their purpose through chatting using AI chatbot. Furthermore, it is possible to check real-time information about restaurants, make reservations, and write reviews. The proposed app uses a collaborative filtering recommendation system, and users receive information on dining out using artificial intelligence chatbots. Through chatbots, users can receive customized services using personal information while minimizing time and space limitations.