• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.17-25
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• 2008

The smart home environment focuses on recognizing the context and physical entities. And this is mainly focused on the personalized service supplied conversational interactions. In this paper, we proposed the recommendation using the context awareness based information filtering that dynamically applied by the context awareness as well as the meta data in the smart home. The proposed method defined the context information and recommended the profited service for the user’s taste using the context awareness based information filtering. Accordingly, the satisfaction of users and the quality of services will be improved the efficient recommendation by supporting the distributed processing as well as the mobility of services. Finally, to evaluate the performance of the proposed method, this study applies to MovieLens dataset in the OSGi framework, and it is compared with the performance of previous studies.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.26-31
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• 2010

This research does focus on realization of customizing recommendation service that all of formal, or informal learning is accomplished at real time according to worker's current situation or business context corresponding with the individual ability and the learning progress at industry or education field. For this, we designed the customizing intelligent recommendation system based on compound knowledge that workers can listen to coaching advices at real time and to retrieve and recommend multidimensional relation easily. Also, we constructed the repository based on compound knowledge and process engine for efficient management of compound knowledge. In specific industry, expert solution or coaching service will be created using the knowledge which is accumulated in long-term.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.1-10
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• 2015

We propose and implement a personalized tour recommendation system based on ontology. We utilize user's profile, dynamic information on search in the application, web search, and facebook for personalized recommendation. We construct tour database for England based on ontology for a demo service, and recommend tour spot considering an individual preference with tour database. This dynamic and personalized tour service makes it possible for individual to plan one's own tour by considering recommended tour spots for each individual.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1446-1465
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• 2022

Diversified SaaS applications allow users more choices to use, according to their own preferences. However, the diversification of SaaS applications also makes it impossible for users to choose the best one. Furthermore, users can't take advantage of the functionality between SaaS applications. In this paper, we propose a platform that provides an SaaS mashup service, by extracting interoperable service functions from SaaS-based applications that independent vendors deploy and supporting a customized service recommendation function through log data binding in the cloud environment. The proposed SaaS mashup service platform consists of a SaaS aggregation framework and a log data binding framework. Each framework was concreted by using Apache Kafka and rule matrix-based recommendation techniques. We present the theoretical basis of implementing the high-performance message-processing function using Kafka. The SaaS mashup service platform, which provides a new type of mashup service by linking SaaS functions based on the above technology described, allows users to combine the required service functions freely and access the results of a rich service-utilization experience, using the SaaS mashup function. The platform developed through SaaS mashup service technology research will enable various flexible SaaS services, expected to contribute to the development of the smart-contents industry and the open market.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.510-523
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• 2012

The purpose of this study is to evaluate an airline service training program based on the CIPP model. Evaluation areas were divided into context, input, process, and product. We analyzed the factors which influenced program satisfaction and recommendation of the training program. Two hundred and one learners who participated in an airline service training program were selected for a survey. The results of this study are as follows. The factors which positively influenced training satisfaction were educational goals in context evaluation, interaction between learners and instructors, managing programs in process evaluation, and training performance in product evaluation. The factor which negatively influenced training satisfaction was human resources in input evaluation. On the other hand, the factors which positively influenced training recommendation were educational goal, assessing needs in context evaluation, interaction between learners and instructors, supporting programs in process evaluation, and training performance in product evaluation. The factor which negatively influenced training recommendation was assessing needs in context evaluation. The results of this study are expected to make an important contribution to the development of service training programs in airlines.

• Knowledge Management Research
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• v.21 no.4
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• pp.175-193
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• 2020

With the proliferation of information technology communication and smart device, the environment where contents are produced and distributed is changing. People can use the contents quickly and easily, and the content industry is attracting attention and creating newly added value by converging with other industries. Accordingly, there is a need for content-related companies to understand the quality of content perceived by users in order to succeed in content, and to use it strategically. Therefore, this study aims to examine the relationship between content quality factors, user satisfaction, and recommendation intention through empirical analysis based on an IS success model. The analysis was conducted using smartPLS3.0 based on a total of 301 survey responses. As a result of the study, it was found that content usefulness, accessible system quality, convenient system quality, service provider trust, and interaction had a significant effect on user's satisfaction. Perceived privacy protection had a significant effect on user satisfaction and recommendation intention. Lastly, it was found that user satisfaction had a significant effect on recommendation intention. The results of this study are expected to provide useful information and therefore content companies can understand about the quality perceived by users.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.475-486
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• 2010

Contents Recommender System predicts user's preferences towards contents, and then recommends highly-predicted contents to user. Digital Identifier plays its part in identifying abstract works or digital contents in digital network environment. Digital Identifier could be effectively used in content-based filtering and collaborative filtering that are mainly used in Contents Recommender Systems. Therefore, this paper proposes an improvement of UCI metadata and resolution service for effective use of UCI in massive contents recommender systems. UCI metadata is expanded by adding elements such as abstract, keyword, genre, age, rate and review. Resolution service allows the operation systems to collect user preference for content by including input part of preference in a result page. This paper also designs and implements an improved UCI operation system and shows that the proposed improvement of UCI metadata and resolution service could be used for massive contents recommendation.

• ETRI Journal
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• v.33 no.4
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• pp.589-599
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• 2011

This paper presents an image description and matching scheme using synthetic features for a recommendation service. The recommendation service is an example of smart search because it offers something before a user's request. In the proposed extraction scheme, an image is described by synthesized spatial and statistical features. The spatial feature is designed to increase the discriminability by reflecting delicate variations. The statistical feature is designed to increase the robustness by absorbing small variations. For extracting spatial features, we partition the image into concentric circles and extract four characteristics using a spatial relation. To extract statistical features, we adapt three transforms into the image and compose a 3D histogram as the final statistical feature. The matching schemes are designed hierarchically using the proposed spatial and statistical features. The result shows that each feature is better than the compared algorithms that use spatial or statistical features. Additionally, if we adapt the proposed whole extraction and matching scheme, the overall performance will become 98.44% in terms of the correct search ratio.

• Science of Emotion and Sensibility
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• v.9 no.spc3
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• pp.179-185
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• 2006

This study examines and compares various recommendation techniques which have been used successfully in other fields and seeks for opportunity to improve design personalization service more effectively. Throughout the literature study, several major recommendation techniques were identified, namely 'contents-based filtering', 'collaborative filtering', and 'demographic filtering'. In order for finding out relative advantages and disadvantages, a case study was carried out by applying different techniques. The result showed that in general, demographic filtering was evaluated least efficient among the techniques. Content-based filtering showed the best efficiency among them. Another significant finding was that the collaborative filtering had a better efficiency as the number of test subjects is increased. In conclusion, we suggest that design recommendation services can be improved by applying contents-based or collaborative filtering for better efficiency of recommendation. And, if the number of test subjects is large enough, it may be possible to remarkably improve the efficiency of design recommendation services by using collaborative filtering.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.660-675
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• 2018

This study is designed to investigate how the perceived characteristics of the online video services affect the 'flow' as positive experience and satisfaction of users. For the study, we conducted a questionnaire survey on 289 people using YouTube, and then analyzed the relationships among variables using hierarchical regression analysis. As a result, it was confirmed that interactivity, newness of recommendation service, diversity of content, and entertainingness of contents all affect the lower level of flow experience. On the other hand, the accuracy of the recommendation service did not affect the flow experience, but positively affects the level of satisfaction. Finally, it is also confirmed that flow has a direct effect on user satisfaction, and mediates relationship between the characteristics of YouTube and satisfaction. The results of this study are helpful to understand user's perception and experience of online video platform service and suggest the discussion points to be considered by the industry to satisfy users.