• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.175-176
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• 2014

• Review of Korea Contents Association
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• v.15 no.3
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• pp.14-20
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• 2017

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.523-528
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• 2021

Current YouTube recommends similar contents to users based on the contents they actually consumed. Due to the feature of these algorithms, users are well recommended for contents in similar fields, but it is difficult to be recommended contents in fields that have never been consumed. There is a limit to being widely recommended for videos. I want to solve this problem by utilizing crowd sourcing. I propose a platform that can be recommended for various channels, through direct participation of the public people using youtube. Users can be recommended a variety of channels, communicate with people in the channel discussion room, and at the same time generate revenue by recommending channels. I hope that this platform can be used in various crowd sourcing-based recommendation platforms.

• Science of Emotion and Sensibility
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• v.9 no.spc3
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• pp.179-185
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• 2006

This study examines and compares various recommendation techniques which have been used successfully in other fields and seeks for opportunity to improve design personalization service more effectively. Throughout the literature study, several major recommendation techniques were identified, namely 'contents-based filtering', 'collaborative filtering', and 'demographic filtering'. In order for finding out relative advantages and disadvantages, a case study was carried out by applying different techniques. The result showed that in general, demographic filtering was evaluated least efficient among the techniques. Content-based filtering showed the best efficiency among them. Another significant finding was that the collaborative filtering had a better efficiency as the number of test subjects is increased. In conclusion, we suggest that design recommendation services can be improved by applying contents-based or collaborative filtering for better efficiency of recommendation. And, if the number of test subjects is large enough, it may be possible to remarkably improve the efficiency of design recommendation services by using collaborative filtering.

• 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.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.11-20
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• 2013

Recently, as SNS services have been increased, studies on recommendation schemes have been actively done. Recommendation scheme provides various favorable or needed services with users on real time. Group recommendation provides users with suitable groups based on their preference. In this paper, we propose a new group recommendation scheme considering user profiles and collaborative filtering in social networks. The proposed scheme can solve the problems of the static profile based group recommendation scheme because it collects the recent group activities and updates user profiles. It also recommends the more various groups by reflecting the similar tendencies of other users within a group through collaborative filtering. Our experimental results show that the proposed scheme recommends various groups that significantly considers the user's changing preferences compared to the existing scheme.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.323-330
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• 2004

As mobile market grows more and more fast, the mobile contents market, especially music contents for mobile phones have recorded remarkable growth. In spite of this rapid growth, mobile web users experience high levels of frustration to search the desired music. New musics are very profitable to the content providers, but the existing collaborative filtering (CF) system can't recommend them. To solve these problems, we propose an extended CF system to reflect the user's real preference by representing the characteristics of users and musics in the feature space. We represent the musics using the music contents based acoustic features in multi-dimensional feature space, and then select a neighborhood with the distance based function. Furthermore, this paper suggests a recommendation for procedure for new music by matching new music with other users' preference. The suggested procedure is explained step by step with an illustration example.

• 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.

• The Journal of the Korea Contents Association
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• v.6 no.4
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• pp.55-62
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• 2006

Important issues emerging with the opening of the ubiquitous age are how to present ubiquitous environment and how services and access methods can be provided to users. The present research proposes a system that can provide users with useful information dynamically through intelligent multi agents in mobile environment. The system is composed of profile module, rule generation module, filtering module and service module. It was designed to find users' demands in an intelligent way based on information on users registered through the recommendation agent. We implemented an applied system and proved its performance through an experiment.