• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.503-505
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• 2017

최근 길거리나 지하철 등에서 스마트폰을 사용하는 사람을 쉽게 찾을 수 있다. 이러한 스마트폰은 대부분 iOS나 안드로이드 운영체제를 사용한다. 따라서 스마트폰에서 사용하는 앱들은 앱스토어나 구글 플레이에서 받아서 사용한다. 하지만, 필요한 앱을 검색해도 비슷한 앱이 많아서 어떤 것을 사용해야 할지 망설이는 경우가 발생한다. 사용자 평점을 기준으로 앱을 선택한다 하더라도 총 누적 평점이기 때문에 현재 버전의 앱이 실제로 어떨지는 알기 어렵다. 이 논문에서는 사용자가 검색한 단어를 바탕으로 구글 플레이 상의 앱을 추천해주는 시스템을 소개한다. 이 시스템은 검색된 최신 버전의 앱에 대한 평점과 사용자 평가를 종합 및 분석하여 사용자에게 추천한다.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.498-503
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• 2018

본 논문은 한국관광공사에서 제공하는 Tour API 3.0 Open API에서 제공하는 데이터를 바탕으로 한다. Google에서 제공해 주는 TensorFlow를 통해서 인공 신경망 딥러닝 알고리즘과 가중치 알고리즘을 통해서 사용자 기호에 맞춰 정보를 추천해 주는 어플리케이션 '눈치코칭_여행딥러닝'의 설계 및 구현에 대하여 서술한다. 현재 순위알고리즘은 평균적으로 40%, 딥러닝 모델은 60%정확도를 보여, 딥러닝이 보다 좋은 성능을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1492-1493
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• 2011

데이트코스 추천 앱은 스마트폰의 휴대성을 이용하여 손쉽게 데이트 코스를 결정할 수 있도록 도와준다. 본 논문은 스마트폰에서 데이터 마이닝 기법을 이용하여 사용자가 원하는 지역, 성별, 연령대, 가격대 등을 선택하면 그 정보에 따라 그 사용자에게 가장 알맞은 데이터코스를 추천하는 앱이다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1475-1480
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• 2014

Wellness is IT fused with the user manage and maintain the health of a service can help you. If you are using an existing Fitness Center to yourself by choosing appliances that fit with the risk of injury in order to learn how the efficient movement had existed for a long time was needed. To resolve, use the personal training but more expensive cost of people's problems, and shown again in the habit of exercising alone will have difficulty. This paper provides a variety of smart phones based on a hybrid app with compatibility with the platform and personalized training market system. Users of the Fitness Center is built into smart phones in the history of their movement sensors or transmits to the Web by typing directly. This is based on exercise programs tailored to users via the training market. Personal training marketplace has a variety of users, check the history of this movement he can recommend an exercise program for themselves can be applied by selecting the. This provides users with the right exercise program can do long-term exercise habits can be proactive and goal setting.

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

• Journal of Digital Convergence
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• v.13 no.8
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• pp.261-265
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• 2015

When choosing a gift for the other person what does that person like? It falls into a happy worry. Choosing the right gift for someone else associated with me is not easy in the personalized world gradually. In this paper a smart phone app is developed for reducing conflicts and saving time to choose gifts. It may help improve a person's satisfaction. It can reduce the worry and time required for buying a gift. It may also be used to make future interpersonal relationships and vitalize the relationships. For the gift recommendation in accordance with the preferences of the person through the process in accordance with the classification of each category can recommend an appropriate gift. The appropriation of the gift is updated to reflect the continuing satisfaction by recommendation status decided by the pre-survey. Any gift is recommended using the formula calculating the priority to recommend reflect a variety of weights. In addition, it is possible to increase the utilization of the app via the fusion between the e-commerce system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.392-394
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• 2021

This paper is a thesis on an automatic restaurant recommendation application for tourists traveling to travel destinations. when you run the application at any travel destination in KOREA, it is an application that recommends desired services such as Korean, Chinese, Western, etc, regardless of the type of food, so that restaurant rankings are poured out in tourist destinations. not only recommending restaurants, but also collecting related information DB so that you can easily find restaurants in tourist destinations through reviews and stars such as hygiene conditions, prices, and compliance with quarantine regulations due to the recent coronavirus. the application was developed

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.19-20
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• 2014

스마트폰의 확산과 그에 따른 모바일 앱 산업의 괄목할 성장과 발맞추어 대학생들의 스마트폰 어플리케이션 선택 이유에 대한 조사를 실시한다. 본 연구에서는 스마트폰 어플리케이션의 선택 이유에 대한 양적인 조사를 연구 범위로 하고 조사방법론으로는 대전의 한 대학교의 학생들에게 설문을 통하여 어플리케이션을 선택하는 이유를 파악하는 방법을 취한다. 또한 어플리케이션 선택 이유에 있어 성별에 따른 차이와 학년에 따른 차이를 SPSS 20 통계패키지를 이용하여 검증한다. 조사 결과는 어플리케이션 선택 이유로 "포털 등에서 정보를 취득하는 경우"와 "친구 등 타인의 권유"가 근소한 차이로 1, 2위로 꼽히고 "앱스토아의 추천"이 가장 낮은 비중을 나타난다. 성별 어플리케이션 선택 이유는 여자가 남자에 비해 친구 등 타인의 권유에 훨씬 크게 영향을 받는 등 통계적으로 유의한 차이를 보인다. 반면 학년별 선택 이유는 통계적으로 유의한 차이를 보이지 않는다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.120-123
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• 2020

보편적으로 사용되는 경로 검색 앱은 출발지에서 목적지까지의 최단 거리 또는 최소 시간을 기준으로 도보 경로를 추천하고 있어 사용자의 다양한 요구사항을 반영하는데 한계가 있다. 이러한 문제를 해결하기 위하여 본 논문에서는 사용자의 일정 및 본인의 섭취한 칼로리를 기준으로 출발지와 목적지 사이의 다수의 경로를 추천하는 앱을 제안하였다. 또한, 제안된 앱의 동작을 검증하기 위하여 해당 기능을 직접 구현하고 다양한 테스트시나리오들을 이용하였다.