• Journal of Broadcast Engineering
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• v.14 no.5
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• pp.625-637
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• 2009

With advent of TV environment and increasing of variety of program contents, users are able to experience more various and complex environment for watching TV contents. According to the change of content watching environment, users have to make more efforts to choose his/her interested TV program contents or TV channels than before. Also, the users usually watch the TV program contents with their own regular way. So, in this paper, we suggests personalized TV program schedule recommendation system based on the analyzing users' TV watching history data. And we extract the users' watched program patterns using the sequential pattern mining method. Also, we proposed a new sequential pattern mining which is suitable for TV watching environment and verify our proposed method have better performance than existing sequential pattern mining method in our application area. In the future, we will consider a VoD characteristic for extending to IPTV program schedule recommendation system.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.183-191
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• 2017

In order to secure the convenience of information retrieval by users of scientific information service platforms and to reduce the time required to acquire the proper information, this study proposes an optimized content recommendation algorithm among the algorithms that currently provide service menus and content information for each service, and conducts comparative evaluation on the results. To enhance the recommendation accuracy, users' major items were added to the original algorithm, and performance evaluations on the recommendation results from the original and optimized algorithms were performed. As a result of this evaluation, we found that the relevance of the content provided to the users through the optimized algorithm was increased by 21.2%. This study proposes a method to shorten the information acquisition time and extend the life cycle of the results as valuable information by automatically computing and providing content suitable for users in the system for each service menu.

• Journal of the Korean Society for information Management
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• v.23 no.3 s.61
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• pp.91-125
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• 2006

Recent advancements in information technology and the Internet have caused an explosive increase in the information available and the means to distribute it. However, such information overflow has made the efficient and accurate search of information a difficulty for most users. To solve this problem, an information retrieval and filtering system was developed as an important tool for users. Libraries and information centers have been in the forefront to provide customized services to satisfy the user's information needs under the changing information environment of today. The aim of this study is to propose an efficient information service for libraries and information centers to provide a personalized recommendation system to the user. The proposed method overcomes the weaknesses of existing systems, by providing a personalized hybrid recommendation method for multimedia contents that works in a large-scaled data and user environment. The system based on the proposed hybrid method uses an effective framework to combine Association Rule with Collaborative Filtering Method.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.81-90
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• 2012

For digital TV, the recommendation of u-health personalized service of semantic environment should be done after evaluating individual physical condition, illness and health condition. The existing recommendation method of u-health personalized service of semantic environment had low user satisfaction because its recommendation was dependent on ontology for analyzing significance. We propose the personalized service recommendation method based on Naive Bayesian Classifier for u-health service of semantic environment in digital TV. In accordance with the proposed method, the condition data is inferred by using ontology, and the transaction is saved. By applying naive bayesian classifier that uses preference information, the service is provided after inferring based on user preference information and transaction formed from ontology. The service inferred based on naive bayesian classifier shows higher precision and recall ratio of the contents recommendation rather than the existing method.

• Journal of Korea Multimedia Society
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• v.11 no.2
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• pp.257-268
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• 2008

Multimedia recommendation systems analyze user preferences and recommend items(multimedia contents) to a user by predicting the user's preference for those items. Among various kinds of recommendation methods, collaborative filtering(CF) has been widely used and successfully applied to practical applications. However, collaborative filtering has two inherent problems: data sparseness and the cold-start problems. If there are few known preferences for a user, it is difficult to find many similar users, and therefore the performance of recommendation is degraded. This problem is more serious when a new user is first using the system. In this paper, we propose a method of generating additional feature of users and items into CF to overcome the difficulties caused by sparseness and improve the accuracy of recommendation. In our method, we first generate additional features by using the probability distribution of feature values, then recommend items by applying collaborative filtering on the modified data to include additional features. Several experimental results that show the effectiveness of the proposed method are also presented.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.133-146
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• 2004

By the development of the network technology, the types and amount of information that users keep in contact with have been dramatically increased. As a result, users are consuming a lot of time and energy to find needed information. On this, this article presents a new methodology that can efficiently manage their information within small cost by using content-based recommendation method and keyword affinity method. By using keyword affinity method, this methodology solves the content-based recommendation method's weak point that the performance is not good within the environment that the preferences of users are rapidly changing and new contents are created continuously and the accuracy level is low until the information of preferences are sufficiently gathered. This article carried out research on the personal e-mail environment where new information is frequently created and disappeared. Also this article assists folder recommendation for the efficient management of e-mail and verified the methodology mentioned above by an experiment to compare the performance of existing folder recommendation methods with the performance of this new method.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.6
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• pp.269-276
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• 2015

The today's rapid spread of smartphones makes it easier to use SNS. However, it reveals only their daily life or interest. Therefore, it is hard to really get to know the detailed part of multi-user's common interests. This paper proposes a content recommendation system which recommends people wanted by identifying common interests through SNS. Recommendation system includes proposal formula considering people wanted and deviation in group. After simulation, the proposed system provide high-quality adapted contents to many users by recommendation item according to the common interest. Number of cases about formula are four. It recommend contents that they have many number of 'like' and few number of deviation in users. The proposed system proves by simulations of four cases and read user's 'likes' data. It provide high-quality adapted contents to many users by recommendation item according to the common interest.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.970-977
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• 2020

The collaborative filtering recommendation technique has been the most widely used since the beginning of e-commerce companies introducing the recommendation system. As the online purchase of products or contents became an ordinary thing, however, recommendation simply applying purchasers' ratings led to the problem of low accuracy in recommendation. To improve the accuracy of recommendation, in this paper suggests the method of collaborative filtering that analyses product reviews and uses them as a weighted value. The proposed method refines product reviews with text mining to extract features and conducts sentiment analysis to draw a sentiment score. In order to recommend better items to user, sentiment weight is used to calculate the predicted values. The experiment results show that higher accuracy can be gained in the proposed method than the traditional collaborative filtering.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.15-16
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• 2014

• The Journal of the Korea Contents Association
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• v.7 no.10
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• pp.19-26
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• 2007

Since collaborative filtering has used the nearest-neighborhood method based on item preference it cannot only reflect exact contents but also has the problem of sparsity and scalability. The item-based collaborative filtering has been practically used improve these problems. However it still does not reflect attributes of the item. In this paper, we propose the method of associative group using the FP-Tree to solve the problem of existing recommendation system. The proposed makes frequent item and creates association rule by using FP-Tree without occurrence of candidate set. We made the efficient item group using $\alpha-cut$ according to the confidence of the association rule. To estimate the performance, the suggested method is compared with Gibbs Sampling, Expectation Maximization, and K-means in the MovieLens dataset.