• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.60-69
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• 2017

Recently, with the rapid development of internet and smart phones, social network services that can create and share various information through relationships among users have been actively used. Especially as the amount of information becomes enormous and unreliable information increases, expert recommendation that can offer necessary information to users have been studied. In this paper, we propose an expert recommendation scheme considering users' interests, human relations, and response quality. The users' interests are evaluated by analyzing their past activities in social network. The human relations are evaluated by extracting the users who have the same interesting fields. The response quality is evaluated by considering the user's response speed and response contents. The proposed scheme determines the user's expert score by combining the users' interests, the human relations, and the response quality. Finally, we recommend proper experts by matching queries and expert groups. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.835-840
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• 2008

This paper proposes an enhanced recommendation technique for personalized e-commerce portal analyzing various attitudes of customer. The attitudes are classifies into three types such as "purchasing product", "adding product to shopping cart", and "viewing the product information". We implicitly track customer attitude to estimate the rating of products for recommending products. We classified user groups which have similar preference for each item using implicit user behavior. The preference similarity is estimated using the Cross Correlation Coefficient. Our recommendation technique shows a high degree of accuracy as we use age and gender to group the customers with similar preference. In the experimental section, we show that our method can provide better performance than other traditional recommender system in terms of accuracy.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.329-334
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• 2018

According to the textbook utilization survey, university students buy 6.4 books and spend 94,000 won per semester. However, nearly half of books are left unused. Therefore many students buy used textbooks instead of buying new books at a fixed price. The existing used textbook trading platforms support basic functions, but don't support textbook recommendation function and reference book recommendation function. In this paper, we developed a used textbook trading platform BookCue that provides textbook recommendation function, reference book recommendation function, and consignment trading function reflecting the regional characteristics. It is expected that will contribute to reduce university students' burden that buying textbook by forming textbooks trading environment and preserve environment. In the near future our platform will need to expand to a platform that deals with a variety of goods, as well as used textbooks in the region.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.600-602
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• 2015

WHO(World Health Organization) as specified due to the global epidemic of obesity in the nation and the social costs associated with health increase. If treating diseases of the existing research targets the medical field with increasing interest in the welfare and well-being sector due to the improvement in earnings, and gradually change to advance the prevention and management. In this paper, we consider these social changes, we propose a personalized recommendation system fitness. This makes it possible that the recommendation is effective to the movement by the movement mechanism by which user. Mobile sensor is overcome by software and having hardware limitations for this purpose, proposes an optimized sensor control mechanism.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.848-861
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• 2015

In this paper, we introduce a new web-based PR video making service system. Many video editing tools have required tough editing skill or scenario planning stage for a just simple PR video making. Some users may prefer a simple and fast way than sophisticated and complex functionality. To solve this problem, it is important to provide easy user interface and intelligent classification and recommendation scheme. Therefore, we propose a new template classification and recommendation scheme using a topic modeling method. The proposed scheme has the big advantage of being able to handle the unstructured meta data as well as structured one.

• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.83-93
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• 2014

Research into recommendation systems for wellness content has focused on representative research on the convergence of wellness and information technology, as interest in wellness has recently increased. But existing research is not suitable because it uses only one or two of the five wellness areas: physical, emotional, social, intellectual, and spiritual. And It cause decline of reliability and satisfaction for recommendation. Thus, a wellness areal feature analysis and integration management technique is needed. In this paper, suggest meta-data configuration and feature analysis technique of content. Also Cosine similarity of wellness areal features of the content was analyzed by applying a wellness areal score calculated in this way and by suggested wellness areal detailed properties and a measurement system to verify the efficiency of this research. This allows the wellness features of contents analyzed, and even will be able to personalized recommendations service for wellness.

• 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 the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.224-229
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• 2011

Collaborative filtering is used the most for recommendation systems because it can recommend potential items. However, when there are not many items to be evaluated, collaborative filtering can be subject to the influence of similarity or preference depending on the situation or the whim of the evaluator. In addition, by recommending items only on the basis of similarity with items that have been evaluated previously without relation to the present situation of the user, the recommendations become less accurate. In this paper, in order to solve the above problems, before starting the collaborative filtering procedure, we calculated similarity not by comparing all the values evaluated by users but rather by comparing only those users who were above the average in order to improve the accuracy of the recommendations. In addition, in the ceaselessly changing ubiquitous computing environment, it is not proper to recommend service information based only on the items evaluated by users. Therefore, we used methods of calculating similarity wherein the users' real time context information was used and a high weight was assigned to similar users. Such methods improved the recommendation accuracy by 16.2% on average.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.61-77
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• 2003

Due to the wide expansion of the internet, people can freely get information what they want with lesser efforts. However without adequate forms or rules to follow, it is getting more and more difficult to get necessary information. Because of seemingly chaotic status of the current web environment, it is sometimes called "Dizzy web" The user should wander from page to page to get necessary information. Therefore we need to construct system which properly recommends appropriate information for general user. The representative research field for this system is called Recommendation System(RS), The collaborative recommendation system is one of the RS. It was known to perform better than the other systems. When we perform the web user modeling or other web-mining tasks, the continuous feedback data is very important and frequently used. In this paper, we propose a collaborative recommendation system which can deal with the continuous feedback data and tried to construct the web page prediction system. We use a sojourn time of a user as continuous feedback data and combine the traditional model-based algorithm framework with the Support Vector Regression technique. In our experiments, we show the accuracy of our system and the computing time of page prediction compared with Pearson's correlation algorithm.algorithm.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.67-74
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• 2011

Collaborative recommendation is one of the most widely used methods of automated product recommendation in e-Commerce. For analyzing the customer's preference, traditional explicit ratings are less desirable than implicit ratings because it may impose an additional burden to the customers of e-commerce companies which deals with a number of products. Cardinal scales generally used for representing the preference intensity also ineffective owing to its increasing estimation errors. In this paper, we propose a new way of constructing the ordinal scale-based customer profile for collaborative recommendation. A Web usage mining technique and lexicographic consensus are employed. An experiment shows that the proposed method performs better than existing CF methodologies.