• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.399-401
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• 2022

POI (Point-of-Interest) 추천은 다양한 위치 기반 서비스에서 중요한 역할을 있다. 기존 연구에서는 사용자의 모바일 선호도를 모델링하기 위해 과거의 체크인의 공간-시간적 관계를 추출한다. 그러나 사용자 궤적에 숨겨진 개인 방문 경향을 반영할 수 있는 structured feature 는 잘 활용되지 않는다. 이 논문에서는 궤적 그래프를 결합한 시공간 인식 attention 네트워크를 제안한다. 개인의 선호도가 시간이 지남에 따라 변할 수 있다는 점을 고려하면 Dynamic GCN (Graph Convolution Network) 모듈은 POI 들의 공간적 상관관계를 동적으로 집계할 수 있다. LBSN (Location-Based Social Networks) 데이터 세트에서 검증된 새 모델은 기존 모델보다 약 9.0% 성능이 뛰어나다.

• Proceedings of the Korea Information Processing Society Conference
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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.

• 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 Korean Society of Rural Planning
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• v.18 no.4
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• pp.129-140
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• 2012

Land suitability assessment assesses development, farming, and conservation suitability, considering land's soil, location, and possibility for use. It also implement segmentation of management regions into production, conservation, and plan management area. It is evaluated as a very significant system in establishing a land use system of sustainable development and development after planning in the aspect that it can establish proper land use plan. This study developed a recommendation model for development in agent-based model that interacts with surrounding lands. It also tried to summarize the area characteristic analysis and the results of land suitability evaluation, targeting three ri's in Yesan-Gun, and analyze the model's applicability by selection results. In order to recommend area for development that considers the use of the surrounding lands, it calculated development possibility indices that considered the ratings of all the lands in the target areas for each parcel and simulated the model. As a result, selected three areas in target region were suitable areas for development in land suitability assessment. In detail, ratings of the recommended parcels were 3, 4, and 5 ratings. As a result of examining the land status, it showed that all the three areas were plan management areas, thus easy for development. It is judged that the model for recommending area for development suggested in this study can be used as important basic data for setting the direction for development when establishing a regional planning.

• Korea Journal of Hospital Management
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• v.21 no.3
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• pp.11-21
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• 2016

The purpose of this study is to investigate the degree of difficulty and turnover of nurses working in the field and to derive clinical work activation and supply policy improvements. Data was collected from December, 2014 to January, 2015, from 23 hospitals, and participants were 3,887 nurses working in the field, Survey details : the difficulty of the clinical work of nurses working in hospitals, turnover intentions status and policy proposals for clinical research work enabled General characteristics, difficulties in clinical working, turnover intention and clinical work plan activation are frequency analysis. The difference between each of the variables in accordance with the general characteristics are one-way ANOVA analysis, Correlation analysis of the variables is also a Pearson correlation coefficients. 'difficulties in clinical working' was a statistically significant difference depending on the type of hospital, nursing class, number of beds, location, age, position, employment, gender, working form, working department, salary, career, and degree level. 'turnover intention' was a statistically significant difference depending on nursing rate, number of beds, region, age, position, sex, shifts, departments, annual income, and career. 'policy recommendation' was a statistically significant difference depending on type of hospital, nursing rate, age, position, employ, shifts, departments, annual income, degree level and career 'difficulties in clinical working' is 'turnover intention' (p<.001), 'policy recommendations' (p<.001) and had a significant positive correlation. and 'turnover intention' had a "policy recommendation" significant positive correlation with the relationship (p<.001) The most difficulties point of the nurses working in the field are the environment, such as shift, urgent and dangerous. Major policy proposals are improve treatment such as wages, and welfare.

• Journal of Biosystems Engineering
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• v.30 no.2 s.109
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• pp.127-133
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• 2005

Precision tillage is designed to till lands variably according to their firmness. Therefore, it is necessary to measure soil firmness in fields and present it in a form with which the variable tillage on be performed. Such forms may be classified into two categories: sensor-based and map-based forms. The map-based approach appears to be inevitable until the technology develops high enough to secure the sensor-based approaches. The first step for map-based precision tillage may be to develop a tillage recommendation map. In this study, a tractor-mountable automatic soil firmness measurement system was developed to construct a cone index map. The system is comprised of three ASAE Standard cone penetrometers and a hydraulic unit for controlling operation of the penetrometers. The system is designed to conduct stop-and-go measurements in fields. The measurements from the three penetrometers are transferred to a microcomputer and the average cone index was calculated. This average cone index was taken as soil firmness of the location where the measurement was made. The cone indices thus determined were used to construct a cone index map using the ArcView software. The system also displays the soil penetration resistance, cone index and soil depth as the cone penetrates into the soil. The field performance of the system was evaluated and the cone index maps at different depths were also presented.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.45-63
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• 2007

Recently as ubiquitous environment comes to the fore, information density is raised and enterprise is being able to capture and utilize customer-related information at the same time when the customer purchases a product. In this environment, a need for the recommender systems which can deliver proper information to the customer at the right time and right situation is highly increased. Therefore, the research on recommender systems continued actively in a variety of fields. Until now, most of recommender systems deal with item recommendation. However, in the market in ubiquitous environment where the same item can be purchased at several stores, it is highly desirable to recommend store to the customer based on his/her contextual situation and preference such as store location, store atmosphere, product quality and price, etc. In this line of research, we proposed the store recommender system using customer's contextual situation and preference in the market in ubiquitous environment. This system is based on collaborative filtering and Apriori algorithms. It will be able to provide customer-centric service to the customer, enhance shopping experiences and contribute in revitalizing market in the long term.

• Information Systems Review
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• v.20 no.3
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• pp.73-92
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• 2018

Many users are uploading their daily life activities on SNS and use hashtags to describe their postings. Hashtag has the advantage of letting users specify categories for their postings, however until now, the users has had to manually input the hashtags which has been very inconvenient for them. Therefore, in order to address this issue, this paper proposes a hashtag recommender system which recommends proper hashtags to users based on their uploaded images on SNS. The proposed system is designed using four analytic structures, which is composed of a camera information-based analysis, an address-based analysis, a location based CF analysis, and an image-based analysis. In order to check whether the proposed system is improved compared to the existing systems in terms of the hashtag recommendation function, we conducted an evaluation with 212 SNS users from fifteen countries. As a result of the evaluation process, the proposed system shows very high accuracy recommendation results compared to the existing hashtag recommender systems.

• 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.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.29-35
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• 2022

UV rays have beneficial or harmful effects on the human body depending on the degree of exposure. An accurate UV information is required for proper exposure to UV rays per individual. The UV rays' information is provided by the Korea Meteorological Administration as one component of daily weather information in Korea. However, it does not provide an accurate UVI at the user's location based on the region's Ultraviolet index. Some operate measuring instrument to obtain an accurate UVI, but it would be costly and inconvenient. Studies which assumed the UVI through environmental factors such as solar radiation and amount of cloud have been introduced, but those studies also could not provide service to individual. Therefore, this paper proposes a deep learning model to calculate UVI using solar object information and sunlight characteristics to provide an accurate UVI at individual location. After selecting the factors, which were considered as highly correlated with UVI such as location and size and illuminance of sun and which were obtained through the analysis of sky images and solar characteristics data, a data set for DNN model was constructed. A DNN model that calculates the UVI was finally realized by entering the solar object information and sunlight characteristics extracted through Mask R-CNN. In consideration of the domestic UVI recommendation standards, it was possible to accurately calculate UVI within the range of MAE 0.26 compared to the standard equipment in the performance evaluation for days with UVI above and below 8.