• 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 Journal of the Korea Contents Association
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• v.13 no.6
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• pp.459-468
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• 2013

This paper is 1) to find out how they recognize medicinal foods, 2) to analyze what make it select for functional restaurants, then which attributes affect on satisfaction and recommendation, 3) and to suggest distinguished guidelines for attracting core customers continuously. The questionnaires are distributed 200 who had only visited in medicinal restaurants and used for data analysis 181 except 19. The findings are summed up as follows; 1. All three factors which are showed 'health managing & prevention', 'functional foods', and 'cuisine presentation' had significant on satisfaction, recommendation, and revisited intention in medicinal restaurant. Among them, 'health managing & prevention' factor had significant highly. 2. Satisfaction variable had affected strongly on recommendation, and also, recommendation had affected on revisited intention.

• 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.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.404-414
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• 2017

With the development of smartphones and online social networks, users produce a lot of contents and share them with each other. Therefore, users spend time by viewing or receiving the contents they do not want. In order to solve such problems, schemes for recommending useful contents have been actively studied. In this paper, we propose a contents recommendation scheme using collaborative filtering for users on online social networks. The proposed scheme consider a user trust in order to remove user data that lower the accuracy of recommendation. The user trust is derived by analyzing the user activity of online social network. For evaluating the user trust from various points of view, we collect user activities that have not been used in conventional techniques. It is shown through performance evaluation that the proposed scheme outperforms the existing scheme.

• 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 Digital Contents Society
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• v.18 no.4
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• pp.707-712
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• 2017

As the amount of data increases exponentially, the recommender system is attracting interest in various industries such as movies, books, and music, and is being studied. The recommendation system aims to propose an appropriate item to the user based on the user's past preference and click stream. Typical examples include Netflix's movie recommendation system and Amazon's book recommendation system. Previous studies can be categorized into three types: collaborative filtering, content-based recommendation, and hybrid recommendation. However, existing recommendation systems have disadvantages such as sparsity, cold start, and scalability problems. To improve these shortcomings and to develop a more accurate recommendation system, we have designed a recommendation system as a factorization machine using actual online product purchase data.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.260-270
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• 2019

As the growth of developing Islamic countries, Muslims are into the world. The most important thing for Muslims to purchase food, ingredient, cosmetics and other products are whether they were certified as 'Halal'. With the increasing number of Muslim tourists and residents in Korea, Halal restaurants and markets are on the rise. However, the service that provides information on Halal restaurants and markets in Korea is very limited. Especially, the application of recommendation system technology is effective to provide Halal restaurant information to users efficiently. The profiling of Halal restaurant information should be preceded by design of recommendation system, and design of recommendation algorithm is most important part in designing recommendation system. In this paper, an Intelligent Recommendation Service system for offering Halal food (IRSH) based on dynamic profiles was proposed. The proposed system recommend a customized Halal restaurant, and proposed recommendation algorithm uses hybrid filtering which is combined by content-based filtering, collaborative filtering and location-based filtering. The proposed algorithm combines several filtering techniques in order to improve the accuracy of recommendation by complementing the various problems of each filtering. The experiment of performance evaluation for comparing with existed restaurant recommendation system was proceeded, and result that proposed IRSH increase recommendation accuracy using Halal contents was deducted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2809-2821
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• 2023

Effective recommendation of similar business groups is a critical factor in obtaining market information for companies. In this study, we propose a novel method for enhancing similar business group recommendation by incorporating derivative criteria and web crawling. We use employment announcements, employment incentives, and corporate vocational training information to derive additional criteria for similar business group selection. Web crawling is employed to collect data related to the derived criteria from 'credit jobs' and 'worknet' sites. We compare the efficiency of different datasets and machine learning methods, including XGBoost, LGBM, Adaboost, Linear Regression, K-NN, and SVM. The proposed model extracts derivatives that reflect the financial and scale characteristics of the company, which are then incorporated into a new set of recommendation criteria. Similar business groups are selected using a Euclidean distance-based model. Our experimental results show that the proposed method improves the accuracy of similar business group recommendation. Overall, this study demonstrates the potential of incorporating derivative criteria and web crawling to enhance similar business group recommendation and obtain market information more efficiently.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.11-24
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• 2019

The tendency of buyers of web information is changing from the cost-effectiveness to the cost-satisfaction. There is such tendency in the recommendation of multimedia contents, some of which are folksonomy-based recommendation services using mood. However, there is a problem that they does not consider synonyms. In order to solve this problem, some studies have solved the problem by defining 12 moods of Thayer model as AV values (Arousal and Valence), but the recommendation performance is lower than that of a keyword-based method at the recall level 0.1. In this paper, we propose a method based on using mood vector of multimedia contents. The method can solve the synonym problem while maintaining the same performance as the keyword-based method even at the recall level 0.1. Also, for performance analysis, we compare the proposed method with an existing method based on AV value and a keyword-based method. The result shows that the proposed method outperform the existing methods.

• 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.