• Journal of Korea Multimedia Society
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• v.18 no.8
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• pp.968-979
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• 2015

In this paper, we study a vibration bell App which shows waiting sequence numbers by utilizing smartphones to solve an inconveniently long waiting time from ordering to getting foods in restaurants and coffee shops. Unlike existing independently developed hardware and software, the vibration bell App is developed to manage and integrate customer management service, POS service in shops, and group shop management services. The functions of the vibration bell App include two-way communications based on NFC- issuing waiting sequence numbers and electronic coupon, showing event information, and transferring user information. Furthermore, the user's personal information is minimized by recognizing the pre-existing information of the user's smartphone. Replacing the shop's vibration bell system with the vibration bell APP, the shops can reduce the cost of construction and maintenance by up to 1/10, compared to the cost of for existing vibration bell systems. Moreover, the customer's preference and current sales trend can be easily figured out. Thus, it will have a great effect on the future marketing strategies.

• 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.

• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.59-66
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• 2015

To date, contents' usability of most multimedia devices has been focused on developer not on user, which made difficult in solving the problems or fulfilling the needs while people using real system. Although user-centered UX and UI researches have been studied and have resulted in innovation in some part, it does not show great effect on usability as it is not easy to interpret human emotions and needs and to apply those to system. Usability is the matter on how deeply smart devices can interpret and analyze human mind not on how much functions and technologies are improved. This study aims to help with usability improvement based on user when people use smart devices in multimedia environment. We studied the interaction between human and contents by analyzing the effect of human emotions and personalities on preference and consumption of contents' type. This study was done by assuming that proper analysis on human emotions may increase user satisfaction on multimedia environment. We analyzed contents preference by gender and emotion. The results showed that there is significant relationship between 'Happy' emotion and 'Comedy Program' preference and men are more prefer it than women. However, it does not reveal any significant relationship between 'Sad' emotion and contents preferences but women are slightly more prefer 'Comedy Program' than men. This result supports the Zillmann's 'mood based management', which suggests that the needs for pleasant contents are revealed to relieve sadness when people are in a sad mood. In addition, our finding corresponds with Oliver's insistence on meeting all four factors, insight, meaningfulness, understanding and reflection, rather than just pleasure for more satisfaction. This study focused on temporary emotional factors and contents and additionally on effect of users' emotion, personality and preference on type of contents consumption. This relationship between emotions and contents study would suggest the better direction for developing smart devices with great contents usability and user satisfaction in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.1075-1078
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• 2008

모바일 웹서비스는 점차 발전하고 있지만, 아직까지 제한된 디바이스의 성능 및 기술적인 요건, 서비스 시스템 통합화의 부재등으로 통해 최적화된 서비스를 제공해 주고 있지 못하다. 따라서 이를 지원하지 위하여 구조화된 서버환경에서 사용자에게 각각의 최적화된 서비스를 제공해 주고, 사용자의 선호나 사용방식에 따라 사용자에게 맞춤형 서비스를 제공해 줄 수 있는 미들웨어 시스템을 제안한다.

• Journal of Broadcast Engineering
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• v.12 no.4
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• pp.321-332
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• 2007

The digitalization of a broadcast has caused the oversupply of the contents in order to fit the user's needs for various broadcast services. The massive contents could not help demanding one-sided watching without considering a taste and preference of a user. This situation is enlarging the demand about the personalized broadcast service to enable user to watch broadcast contents in anytime according to surplus provision of broadcast contents. TV-Anytime standard could be a solution for broadcast service to enable users to watch personalized broadcast contents according to their preference. The paper proposes the personalized broadcasting system for authoring, receiving and transmission of TV-Anytime metadata, the detailed information about the broadcast contents so that user could efficiently search a large of broadcast contents stored in the PDR(Personal Digital Recorder)receiver under the DCATV(Digital Cable Television) environment.

• Fashion & Textile Research Journal
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• v.23 no.1
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• pp.98-105
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• 2021

This study investigates and analyzes user preferences for golf wear with a sense of wear and smart function for the development of smart golf wear based on user convenience. A survey was conducted on 124 males in the age range of 40-60s that consisted of professional golfers, amateur golfers and the public with golf experience (such as major golf consumers) from August 1 to August 30, 2019 (IRB NO. 1040198-190617-HR-057-03); consequently, a 117 copies were accepted for analysis. The findings are as follows. The elbow (4.3%) of golf wear is unsatisfactory. The important part of the golf swing motion is the shoulder (39.3)>, elbow (30.8%)>, and wrist (6.8%). In addition, the unsatisfactory wearing of golf wear due to golf swing movements indicated that the shoulder or elbow area was pulled or the bottom of the top was raised during the back swing movements. The survey results on the expected discomfort when wearing smart wear are 'discomfort of obstruction when wearing' (53.8%), 'discomfort of washing' (17.1%), and 'weight of attached machine' (13.7%). Opinions such as 'Will not feel good when the sensor is attached' were investigated. The examination of the preference for golf wear equipped with smart functions indicated that a posture correction function to correct the golf swing posture is the most desired quality that is also considered important when correcting posture.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.4
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• pp.338-345
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• 2004

Personal Digital Assistants (PDAs) have become ubiquitous and continued to gain popularity. Since PDAs have some special contexts such as mobility and limited screen size, icons are utilized frequently because icons allow us to do tasks more rapidly and effectively on PDAs like another information appliances. The study presents a cognitive approach to study human factors affecting icon design with multidimensional Scaling (MDS) analysis. In the experiment, a real PDA was used to investigate 29 attributes and2 preference ratings for 22 PDA icons by 20 Korean subjects. As a result, cognitive positioning about icons, attributes, and preference data were arranged on the two dimensional perceptual map. Attributes were grouped by simplicity, universality, activity, complexity, abstraction, static, and alphanumeric time. Subjects preferences were highly related with simplicity attributes group and positive to universality and activity attributes groups. It was also confirmed that there are some icons unfitted to the mental model of Korean. However, when icons are designed for PDAs or similar information appliances to Korean, it should be designed simply and actively with universal image fitted on target users mental model.

• Journal of Korean Library and Information Science Society
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• v.50 no.2
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• pp.339-359
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• 2019

The purpose of this study is to clarify the case and process of the application of the sign system in order to enable the user to reach the target site more efficiently in the library. For this purpose, a new sign system was applied to the S University library through procedures such as library structure, sign status, user observation, cyan production and preference survey. Detailed production process and user's reaction were identified. Based on this we suggested improvements to the library stacks signage system.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.346-348
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• 2022

Media Contents Recommendation Web Service (service name 'mobodra') is a web service that analyzes media types and genre tastes for each user and recommends content accordingly. Users select some of the works randomly provided on the web when signing up for membership and analyze their tastes based on this. Based on this analysis, preferred content for each user is recommended. In this paper, we implement a content recommendation algorithm through item-based collaborative filtering. When the user's activity data or preference is re-examined, the above process is executed again to update the user's taste.