• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.221-232
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• 2023

In this paper, we propose a recommendation system based on the latent factor model using matrix factorization, which is one of the most commonly used collaborative filtering algorithms for recommendation systems. In particular, by introducing the concept of creating a list of recommended content and a list of non-preferred recommended content, and removing the non-preferred recommended content from the list of recommended content, we propose a method to ultimately increase the satisfaction. The experiment confirmed that using a separate list of non-preferred content to find non-preferred content increased precision by 135%, accuracy by 149%, and F1 score by 72% compared to using the existing recommendation list. In addition, assuming that users do not view non-preferred content through the proposed algorithm, the average evaluation score of a specific user used in the experiment increased by about 35%, from 2.55 to 3.44, thereby increasing user satisfaction. It has been confirmed that this algorithm is more effective than the algorithms used in existing recommendation systems.

• Journal of Digital Contents Society
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• v.13 no.4
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• pp.559-565
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• 2012

The remarkable development of IT is contributed to popularization of smart phones, which in turn creates a new domain called app store. Smartphone apps have grown fast because they can be easily purchased through an app store. As the volume of apps traded in app stores is so huge that it is extremely hard for users to find the exact app they want. In general, an app store recommends an app to users based on the search words they entered. In terms of recommendation of app, this kind of content-based method is not effective. To increase accuracy in recommending app, this paper proposes a characteristic similarity-based app recommendation method. This method creates attributes on the app based on the related information such as genre, functionality and number of downloads and then compares them with the propensity to use the app. According to diverse simulations, the method proposed in this paper improved the performance of app recommendation by 33% in average, compared to the conventional method.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.95-104
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• 2019

Recently, software education has been emphasized as a key element of the fourth industrial revolution. Many universities are strengthening the software education for all students according to the needs of the times. The use of online content is an effective way to introduce SW education for all students. However, the provision of uniform online contents has limitations in that it does not consider individual characteristics(major, sw interest, comprehension, interests, etc.) of students. In this study, we propose a recommendation method that utilizes the directional similarity between contents in the boolean view history data environment. We propose a new item-based recommendation formula that uses the confidence value of association rule analysis as the similarity level and apply it to the data of domestic paid contents site. Experimental results show that the recommendation accuracy is improved than when using the traditional collaborative recommendation using cosine or jaccard for similarity measurements.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1083-1091
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• 2017

As big data technologies have been developed and massive data have exploded from users through various channels, CEO of global IT enterprise mentioned core importance of data in next generation business. Therefore various machine learning technologies have been necessary to apply data driven services but especially recommendation has been core technique in viewpoint of directly providing summarized information or exact choice of items to users in information flooding environment. Recently evolved recommendation techniques have been proposed by many researchers and most of service companies with big data tried to apply refined recommendation method on their online business. For example, Amazon used item to item collaborative filtering method on its sales distribution platform. In this paper, we develop a commercial web service for suggesting music contents and implement three representative collaborative filtering methods on the service. We also produce recommendation lists with three methods based on real world sample data and evaluate the usefulness of them by comparison among the produced result. This study is meaningful in terms of suggesting the right direction and practicality when companies and developers want to develop web services by applying big data based recommendation techniques in practical environment.

• 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.11 no.11
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• pp.1-9
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• 2011

With the development of next IT convergence technology and the construction of infrastructure for personalized healthcare services, the importance of services based on user's preference is being spotlighted. Healthcare service have been progressed as treatment and management for specific diseases and dietary nutrition managements to customers according to the increase in chronic patients. In this paper, we proposed the personalized dietary nutrition contents recommendation using the hybrid filtering for managing health. The proposed method uses the hybrid filtering through combining the collaborative filtering and the image filtering in order to reinforce the special trend that recommendation provides similar contents. We developed the Web application for this purpose, and experimented with it to verify the logical validity and effectiveness. Accordingly, the satisfaction and the quality of services will be improved the healthcare by recommending the dietary nutrition contents. This evaluation found that the difference of satisfaction by service was statistically meaningful and showed high satisfaction.

• 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.11 no.2
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• pp.22-30
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• 2011

In the IT convergence environment changed with not only the quality but also the material abundance, it is the most crucial factor for the strategy of personalized recommendation services to investigate the context information. In this paper, we proposed the recommendation using the service ontology based context awareness modeling. The proposed method establishes a data acquisition model based on the OSGi framework and develops a context information model based on ontology in order to perform the device environment between different kinds of systems. In addition, the context information will be extracted and classified for implementing the recommendation system used for the context information model. This study develops the ontology based context awareness model using the context information and applies it to the recommendation of the collaborative filtering. The context awareness model reflects the information that selects services according to the context using the Naive Bayes classifier and provides it to users. To evaluate the performance of the proposed method, we conducted sample T-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.346-354
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• 2009

This paper proposes a new learning recommendation system for learning patterns that educators are applying to learners using item-based method. The proposed method in this paper first collects personal learning methods based on learning information that learners are performing through the internet contents site. Then this system recommends a learning method which is estimated most properly to learners after classifying learning elements based on these information. The students of a middle school took part in the experiment in order to evaluate the proposed system, and the students were divided into three groups according to their grades. We gave inter-attribute and intra-attribute weights to learning elements applying to each group for recommending the most efficient method to improve learning achievement. The experiment showed that the learning achievement of learners in the proposed method is improved considerably compared to the previous grades.

• 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.