• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.269-272
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• 2011

추천은 다양한 컨텐츠 중에서 사용자가 원하는 것을 선택할 수 있도록 돕는 것이다. 이러한 추천은 광고주가 자신의 광고에 적절한 컨텐츠를 찾을 때에도 활용될 수 있다. 본 논문에서는 광고를 표현하는 태그와 영화를 나타내는 주제어들을 매칭하여 광고에 적합한 영화를 추천하는 문제를 다룬다. 이 문제의 경우, 광고를 표현하는 태그의 개수가 적고, 영화의 주제어와 성격이 다른 경우가 많아 단순 매칭을 활용한 추천 기법으로는 결과를 얻을 수 없는 경우도 존재한다. 우리는 이러한 문제를 완화하기 위해 키워드 확장을 통한 추천 기법을 제안한다. 구체적으로 각 영화 컨텐츠가 가진 주제어를 위키피디아를 통해 검색하고 이를 통해 주제어를 확장한다. 광고의 태그 또한 위키피디아 검색을 통해 확장한다. 이렇게 확장된 영화 주제어와 광고 태그를 연관성 규칙에 기반하여 매칭한다. 실험 결과 단순 매칭보다 제안한 확장을 통한 매칭이 37.5%의 성능 향상을 보였다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.399-406
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• 2021

With the steady growth of the content industry, the need for research that automatically recommending content suitable for individual tastes is increasing. In order to improve the accuracy of automatic content recommendation, it is needed to fuse existing recommendation techniques using users' preference history for contents along with recommendation techniques using content metadata or features extracted from the content itself. In this work, we propose a new graph-based music recommendation method which learns an LSTM-based classification model to automatically extract appropriate tagging words from sound data and apply the extracted tagging words together with the users' preferred music lists and music metadata to graph-based music recommendation. Experimental results show that the proposed method outperforms existing recommendation methods in terms of the recommendation accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.709-716
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• 2020

In social media, when posting a post, tag information of an image is generally used because the search is mainly performed using a tag. Users want to expose the post to many people by attaching the tag to the post. Also, the user has trouble posting the tag to be tagged along with the post, and posts that have not been tagged are also posted. In this paper, we propose a method to find an image similar to the input image, extract the label attached to the image, find the posts on instagram, where the label exists as a tag, and recommend other tags in the post. In the proposed method, the label is extracted from the image through the model of the convolutional neural network (CNN) deep learning technique, and the instagram is crawled with the extracted label to sort and recommended tags other than the label. We can see that it is easy to post an image using the recommended tag, increase the exposure of the search, and derive high accuracy due to fewer search errors.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.1-13
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• 2013

Recent advance and popularization of smart devices and web application services based on cloud computing have made end-users to directly produce and, at the same time, consume the image contents. This leads to demands of unified contents management services. Thus, this paper proposestag clustering method based on semantic similarity for effective image categorization. We calculate the cost of semantic similarity between tags and cluster tags that are closely related. If tags are in a cluster, we suppose that images with them are also in a same cluster. Furthermore, we could recommend tags for new images on the basis of initial clusters.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.15-24
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• 2013

In recent years, the number of social network system has grown rapidly. Among them, social bookmarking system(SBS) is one of the most popular systems. SBS provides network platform which users can share and manage various types of online resources by using tags. In SBS, it can be possible to reflect tag and time in order to enhance the quality of personalized recommendation. In this paper, we proposed recommender system which reflect tag and time at weight generation and similarity calculation. Also we adapted proposed method to real dataset and the result of experiment showed that the our method offers better performance when such information is integrated.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.13-21
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• 2019

The tendency of buyers of web information is changing from the cost-effectiveness which emphasizes the performance over the price to the cost-satisfaction which emphasizes the psychological satisfaction of the buyer. In music recommendation, one of the methods to increase psychological satisfaction is to use the music mood. In this paper, a music recommendation method considering the mood tag and the synonyms tag is proposed and, as an intermediate result of the proposed method, mood tags and music pieces are expressed in Thayer's AV space and then their distribution are analyzed. The analysis result shows the distributions of mood tags and the ones of music pieces are similar, which implies that the proposed recommendation method can provide significant results. In the future, the music recommendation performance will be analyzed.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.1010-1014
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• 2010

Article-related product recommender system is an emerging e-commerce service which recommends items based on association in contexts between items and articles. Current services recommend based on the similarity between tags of articles and items, which is deficient not only due to the high cost in manual tagging but also low accuracies in recommendation. As a component of novel article-related item recommender system, we propose a new method for tagging item images based on pre-defined categories. We suggest a hypernetwork-based algorithm for learning association between images, which is represented by visual words, and categories of products. Learned hypernetwork are used to assign multiple tags to unlabeled item images. We show the ability of our method with a product set of real-world online shopping-mall including 1,251 product images with 10 categories. Experimental results not only show that the proposed method has competitive tagging performance compared with other classifiers but also present that the proposed multi-tagging method based on hypernetworks improves the accuracy of tagging.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.47-57
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• 2009

Collaborative tagging systems allow users to attach tags to diverse sharable contents in social networks. These tags provide usefulness in reusing the contents for all community members as well as their creators. Three-dimensional data composed of users, items, and tags are used in the collaborative tag-based recommendation. They are generally more voluminous and sparse than two-dimensional data composed of users and items. Therefore, there are many difficulties in applying existing collaborative filtering methods directly to them. Latent factor models, which are also successful in the area of collaborative filtering recently, discover latent features(factors) for explaining observed values and solve problems based on the features. However, establishing the models require much time and efforts. In order to apply the latent factor models to three-dimensional collaborative filtering data, we have to overcome the difficulty of establishing them. This paper proposes various methods for determining preferences of users to items via establishing an intuitive model by assuming tags used for items as latent factors to users and items respectively. They are compared using real data for concluding desirable directions.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.609-612
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• 2010

최근 스마트 폰을 이용한 사용자들이 생성하는 사진 데이터의 양이 급속히 증가하였다. 폭발적인 사진 데이터 양의 증가는 사용자가 원하는 사진에 대한 접근을 어렵게 하였다. 때문에 본 연구에서는 사진의 접근 및 관리의 효율을 높이기 위한 폭소노미를 통한 태그 추천 시스템을 안드로이드 모바일 플랫폼과 서버의 연계로 구현하였다. 구현된 애플리케이션은 25,000 장의 사진을 기반으로 하는 폭소노미를 통해 태그 추천을 하며, 태그 추천에 평균적으로 5.5 초의 시간이 걸렸다.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.133-141
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• 2008

Social Web is turning current Web into social platform for knowing people and sharing information. This paper takes major social tagging systems as examples, namely delicious, flickr and youtube, to analyze the social phenomena in the Social Web in order to identify the way of mediating and linking social data. A simple Tag Ontology (TO) is proposed to integrate different social tagging data and mediate and link with other related social metadata. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tag ontology is also suggested as an applying field.