• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.1-6
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• 2019

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user's favorite the keyword as well as the genre.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1068-1082
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• 2015

Due to the rapid advancement of the mobile technology, smart phones have been widely used in the current society. This lead to an easier way to retrieve video contents using web and mobile services. However, it is not a trivial problem to retrieve particular video contents based on users' specific preferences. The current movie recommendation system is based on the users' preference information. However, this system does not consider any emotional means or perspectives in each movie, which results in the dissatisfaction of user's emotional requirements. In order to address users' preferences and emotional requirements, this research proposes a movie recommendation technology to represent a movie's emotion and its associations. The proposed approach contains the development of emotion ontology by representing the relationship between the emotion and the concepts which cause emotional effects. Based on the current movie metadata ontology, this research also developed movie-emotion ontology based on the representation of the metadata related to the emotion. The proposed movie recommendation method recommends the movie by using movie-emotion ontology based on the emotion knowledge. Using this proposed approach, the user will be able to get the list of movies based on their preferences and emotional requirements.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.133-141
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• 2015

Mass multimedia contents are shared through various social media servies including social network service. As social network reveals user's current situation and interest, highly satisfactory personalized recommendation can be made when such features are applied to the recommendation system. In addition, classifying the music by emotion and using analyzed information about user's recent emotion or current situation by analyzing user's social network, it will be useful upon recommending music to the user. In this paper, we propose a music recommendation method that makes an emotion model to classify the music, classifies the music according to the emotion model, and extracts user's current emotional state represented on the social network to recommend music, and evaluates the validity of our method through experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.446-465
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• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.131-149
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• 2004

Collaborative filtering is one of popular techniques for personalized recommendation in e-commerce sites. An advantage of collaborative filtering is that the technique can work with sparse evaluation data to predict preference scores of new alternative contents or advertisements. There is, however, no in-depth study about the sparsity effect of customer's evaluation data to the performance of recommendation. In this study, we investigate the sparsity effect and hybrid usages of customers' evaluation data and purchase data using an experiment result. The result of the analysis shows that the performance of recommendation decreases monotonically as the sparsity increases, and also the hybrid usage of two different types of data; customers' evaluation data and purchase data helps to increase the performance of recommendation in sparsity situation.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.701-702
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• 2008

A large amount of efforts are required to search user's preferred contents for the program contents being provided by IPTV services. In this paper, using collaborative filtering, an automatic recommendation method of IPTV program contents is presented by reasoning similar group preferences on IPTV program contents which constitutes personalized IPTV environments. The proposed method models the user's preference of IPTV program contents with the program attributes such as content, genres, channels actor/actress, staffs and calculates it using the watching history of program contents in different genres and watching times. Also, the proposed method considers timely changing user's preference and the preference oon the content itself, which improves the traditional collaborative filtering methods that can not recommend the non-consumed items.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.399-406
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• 2021

With the steady growth of the content industry, the need for research that automatically recommending content suitable for individual tastes is increasing. In order to improve the accuracy of automatic content recommendation, it is needed to fuse existing recommendation techniques using users' preference history for contents along with recommendation techniques using content metadata or features extracted from the content itself. In this work, we propose a new graph-based music recommendation method which learns an LSTM-based classification model to automatically extract appropriate tagging words from sound data and apply the extracted tagging words together with the users' preferred music lists and music metadata to graph-based music recommendation. Experimental results show that the proposed method outperforms existing recommendation methods in terms of the recommendation accuracy.

• The Korean Journal of Franchise Management
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• v.10 no.4
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• pp.43-52
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• 2019

Purpose: As a research on the effects of the characteristics of franchise educational institution and contents on the educational transition, commitment, and recommendation intention, this study aimed to suggest the basic data that could be used for the performance of educational training of franchise headquarters, and also to suggest an empirical research helpful for the development of actual educational system and the operation of curriculum for franchise educational institutions. Research design, data, and methodology: This study selected the trainees who recently completed the training in a franchise educational institution as the samples. The survey was conducted for 20 days from October 1st to October 20th 2018, targeting total 230 people, and total 207 questionnaires were collected (Missing value 23). To verify the validity of the measurement tool used for this study, this study reviewed the factor loading of each factor by conducting the confirmatory factor analysis(CFA), and then verified the average variance extracted(AVE) and the composite construct reliability(CCR). Lastly, the structural equation model(SEM) was verified based on the research hypotheses and research model. The SPSS Win Ver. 20.0 & AMOS 20.0 were used for every analysis of this study. Results: The results of this study could be summarized as follows. First, the reputation and interaction of the characteristics of franchise educational institution had significantly positive(+) effects on the educational transition. Second, all the sub - variables of educational contents such as job relevance, education method, and instructors' professionalism had positive(+) effects on the educational transition while the educational transition had positive effects on the organizational commitment, career commitment, and job commitment. Lastly, the organizational commitment and job commitment had positive(+) effects on the recommendation intention. Thus, the trainees with higher organizational commitment and job commitment in a franchise educational institution, showed higher intention to recommend the educational institution to others. Conclusions: The results of this study imply that the franchise educational institutions could increase the actual performance of education such as educational transition, commitment, and recommendation intention by increasing interactions within educational institutions and also designing effective educational contents, so that the trainees could highly perceive the educational transition of education.

• 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.