• Annual Conference of KIPS
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• 2004.11a
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• pp.753-756
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• 2004

유비쿼터스 컴퓨팅 환경에서 추천시스템은 무수히 많은 정보들에 대하여 사람들이 적절한 선택을 할 수 있도록 도와준다. 사용자에게 필요한 정보를 찾아주고, 정보들의 우선순위를 결정해주는 추천시스템에 있어서 사용자의 위치는 보다 가치있는 정보를 제공할 수 있는 도구가 된다. 위치기반 추천시스템은 사용자가 아이템들로부터 얼마나 멀리 떨어져있는가를 고려하여 상위 리스트들을 제공할 수 있어야 한다. 하지만 일반적인 추천시스템에서 주로 사용되고 있는 기존의 사용자 기반 협업필터링 기법은 사용자의 자발적인 정보 입력에 의존함으로써 일정한 수의 사용자 정보가 축적되어 있지 않으면 정확한 추천이 불가능한 단점이 있다. 본 논문에서는 아이템에 기반한 협업 필터링 기법을 확률적으로 분석하고, 아이템의 위치에따라 랭킹을 부여하는 방법과 사용자의 위치정보를 추천알고리즘에 적용시켜 보다 정확하고 효율적인 추천방법을 제안하였다.

• Annual Conference of KIPS
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• 2018.10a
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• pp.856-859
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• 2018

사용자 위치 기반 방문지 장소 추천에 대한 연구가 활발히 진행되었다. 기존 장소 추천 모델은 모든 사용자가 방문지 선택을 결정하는 항목에 동일한 가중치를 부여한다고 가정하여 주어진 정보안에서 정확도를 최대화 시키는데 초점이 맞춰져 있다. 이러한 모델은 사용자의 항목별 선호도가 모델의 가정과 일치하지 않을 때 추천 정확도가 급감한다는 문제점을 가진다. 본 연구에서는 방문지 선택을 결정하는 항목에 가중치를 사용자가 직접 설정하고, 이를 토대로 장소를 추천하는 모델 및 알고리즘을 제안 및 구현한다. 제안하는 기법을 통해, 상이한 요구사항을 지닌 방문 후보자가 자신의 경향성을 반영한 장소를 추천 받는 서비스를 제공할 수 있다.

• Annual Conference of KIPS
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• 2015.10a
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• pp.523-526
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• 2015

이 논문에서는 사용자 위치 설정에 따른 위치 기반 어플리케이션 추천 서비스를 제안한다. 현재 스마트폰의 사용은 일상화되어 있으며 스마트폰 사용자 대부분은 많은 어플리케이션을 설치하여 이용하고 있다. 많은 어플리케이션 사용은 몇 가지 불편함을 초래한다. 우선 사용자들이 스마트폰 사용 시 위치와 상황에 맞는 어플리케이션을 사용하기 위해서 해당 어플리케이션을 찾아야하며, 여러 번의 화면 조작을 해야 한다는 불편함이 있다. 이 논문에서는 사용자의 특정 위치나 상황에 맞는 어플리케이션 사용을 위하여 사용자의 위치 설정을 통한 어플리케이션 추천 서비스를 제공한다. 이 서비스는 사용자의 위치와 사용자 설정 위치들 중 근접한 위치를 선택하여 그 설정 위치에서의 어플리케이션 사용량을 수집함으로써 사용자에게 필요한 어플리케이션을 추천한다.

• Annual Conference of KIPS
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• 2009.04a
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• pp.535-538
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• 2009

모바일 단말기 사용자 수의 증가와 위치기반 서비스 기술의 발달로 위치 정보를 활용한 다양한 위치 정보 서비스가 등장하고 있다. 친구들과 약속을 정하는 일은 빈번하게 일어난다. 약속을 정하기 위해서는 모든 친구에게 연락을 해야 하고 각자 선호하는 장소가 다르기 때문에 모든 친구들이 만족할 만한 최적의 장소를 찾기가 어렵다. 본 논문에서는 모바일 환경에서 친구의 위치를 파악하고 사용자와 친구의 성향을 파악하기 위해 협업 필터링과 인구통계학적 정보를 사용하였고, 약속 장소를 선정하기 위해 사용자와 친구의 위치를 기반으로 후보 영역을 선택하여 약속 장소와 시간을 추천하는 서비스를 제공함으로써 약속을 맺기 위한 절차를 간소화 할 뿐 아니라 사용자와 친구의 성향에 맞는 약속 장소를 추천하여 사용자와 친구가 만족 할 수 있는 약속을 형성할 수 있도록 하는 약속 장소, 시간 추천 서비스 시스템을 설계하였다.

• Annual Conference of KIPS
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• 2008.05a
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• pp.40-43
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• 2008

This paper describes a Grid-based context-aware doctor recommender system which recommends appropriate doctors for a patient or user at the right time in the right place. The core of the system is a recommendation mechanism that analyzes a user's demographic profile, user's current context information (i.e., location, time, and weather), and user's position so that doctor information can be ranked according to the match with the preferences of a user. The performance of our architecture is evaluated compare to centralized recommender system.

• Annual Conference of KIPS
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• 2007.11a
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• pp.436-439
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• 2007

추천 서비스는 사용자에게 적합한 서비스를 선응적으로 제공하는 기술로써, 전자상거래 환경을 중심으로 널리 이용되고 있다. 그러나, 유비쿼터스 환경에서도 가장 활발한 기술 접목이 이루어지는 홈 네트워크 환경 내에 추천 서비스가 적용된 사례는 많지 않다. 본 논문에서는 홈 네트워크 환경에서 누적된 사용자와 기기 간 상호작용 정보들을 바탕으로 사용자 위치 기반의 개인화된 서비스를 추천하는 알고리즘을 제안한다. 본 알고리즘에서는 밀도기반 초기값 선정 기법을 적용한 군집화를 통해 필요한 데이터만을 추출함으로써 서비스 추천의 효율성 및 정확성을 높인다. 또한, 사용자 기반의 협업 필터링을 이용하여 데이터가 충분히 많지 않은 상황에서도 정확한 서비스 추천을 수행한다.

• Asia pacific journal of information systems
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• v.29 no.3
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• pp.468-488
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• 2019

As the growth of online travel agencies (hereafter OTAs) accelerates, competition among hotels to gain exposure on the first page of OTA websites, and the financial burden, such as commissions hotels have to pay in return, are increasing. Therefore, to facilitate successful management in the tourism industry, it is important to establish what makes people continue the practice of using OTAs to book rooms in hotels and other accommodation outlets. By adopting the heuristic-systematic model (HSM), this study explores the factors that drive consumers' continued use of OTA and classifies them into heuristic cues (brand awareness, cost saving, and scarcity message) and systematic cues (recommendation quality and the ability to provide reputation). Furthermore, we divided the sample based on the location of hotels within and outside Korea, and investigated the different roles of the cues between two models. The results are expected to provide theoretical and practical implications for both OTAs and hotels.

• Journal of Convergence for Information Technology
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• v.7 no.3
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• pp.165-171
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• 2017

Recently, the development and dissemination of smart devices linked to the network are being actively performed, and IOT(Internet of things) is issued form of mutually cooperative relationship through interoperation within smart devices. In this paper, we propose an innovative intelligent coffee shop management system by sharing and cooperatively controlling smart devices and Internet devices. The system can order smartphones including kiosk orders and beacon-based user identification. And it is a system that can make custom order without grasping user location information using geofence. The paper provides weather, temperature, time and user-based recommendation services based on Big Data. Therefore, the system is increased cost reduction and work efficiency than general coffee shops.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.191-210
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• 2014

As mobile devices that can be connected to the Internet have spread and networking has become possible whenever/wherever, the Internet has become central in the dissemination and consumption of news. Accordingly, the ways news is gathered, disseminated, and consumed have changed greatly. In the traditional news media such as magazines and newspapers, expert editors determined what events were worthy of deploying their staffs or freelancers to cover and what stories from newswires or other sources would be printed. Furthermore, they determined how these stories would be displayed in their publications in terms of page placement, space allocation, type sizes, photographs, and other graphic elements. In turn, readers-news consumers-judged the importance of news not only by its subject and content, but also through subsidiary information such as its location and how it was displayed. Their judgments reflected their acceptance of an assumption that these expert editors had the knowledge and ability not only to serve as gatekeepers in determining what news was valuable and important but also how to rank its value and importance. As such, news assembled, dispensed, and consumed in this manner can be said to be expert-based recommended news. However, in the era of Internet news, the role of expert editors as gatekeepers has been greatly diminished. Many Internet news sites offer a huge volume of news on diverse topics from many media companies, thereby eliminating in many cases the gatekeeper role of expert editors. One result has been to turn news users from passive receptacles into activists who search for news that reflects their interests or tastes. To solve the problem of an overload of information and enhance the efficiency of news users' searches, Internet news sites have introduced numerous recommendation techniques. Recommendations based on popularity constitute one of the most frequently used of these techniques. This popularity-based approach shows a list of those news items that have been read and shared by many people, based on users' behavior such as clicks, evaluations, and sharing. "most-viewed list," "most-replied list," and "real-time issue" found on news sites belong to this system. Given that collective intelligence serves as the premise of these popularity-based recommendations, popularity-based news recommendations would be considered highly important because stories that have been read and shared by many people are presumably more likely to be better than those preferred by only a few people. However, these recommendations may reflect a popularity bias because stories judged likely to be more popular have been placed where they will be most noticeable. As a result, such stories are more likely to be continuously exposed and included in popularity-based recommended news lists. Popular news stories cannot be said to be necessarily those that are most important to readers. Given that many people use popularity-based recommended news and that the popularity-based recommendation approach greatly affects patterns of news use, a review of whether popularity-based news recommendations actually reflect important news can be said to be an indispensable procedure. Therefore, in this study, popularity-based news recommendations of an Internet news portal was compared with top placements of news in printed newspapers, and news users' judgments of which stories were personally and socially important were analyzed. The study was conducted in two stages. In the first stage, content analyses were used to compare the content of the popularity-based news recommendations of an Internet news site with those of the expert-based news recommendations of printed newspapers. Five days of news stories were collected. "most-viewed list" of the Naver portal site were used as the popularity-based recommendations; the expert-based recommendations were represented by the top pieces of news from five major daily newspapers-the Chosun Ilbo, the JoongAng Ilbo, the Dong-A Daily News, the Hankyoreh Shinmun, and the Kyunghyang Shinmun. In the second stage, along with the news stories collected in the first stage, some Internet news stories and some news stories from printed newspapers that the Internet and the newspapers did not have in common were randomly extracted and used in online questionnaire surveys that asked the importance of these selected news stories. According to our analysis, only 10.81% of the popularity-based news recommendations were similar in content with the expert-based news judgments. Therefore, the content of popularity-based news recommendations appears to be quite different from the content of expert-based recommendations. The differences in importance between these two groups of news stories were analyzed, and the results indicated that whereas the two groups did not differ significantly in their recommendations of stories of personal importance, the expert-based recommendations ranked higher in social importance. This study has importance for theory in its examination of popularity-based news recommendations from the two theoretical viewpoints of collective intelligence and popularity bias and by its use of both qualitative (content analysis) and quantitative methods (questionnaires). It also sheds light on the differences in the role of media channels that fulfill an agenda-setting function and Internet news sites that treat news from the viewpoint of markets.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.