• Journal of Digital Contents Society
• /
• v.8 no.3
• /
• pp.255-262
• /
• 2007

Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs, videos and other content. In this paper, we analyze the structure and basic knowledge of collaborative tagging systems as well as their dynamical aspects. We also present a retrieval system, TagPlus, using synonym tag that is derived from WordNet database. Specifically, TagPlus, a synonym tag based system has users retrieve images from Flickr system. The proposed system show the images tagged by not only the tag that users input but also the synonyms that are synonyms with the tag.

• Journal of Intelligence and Information Systems
• /
• v.14 no.2
• /
• pp.157-177
• /
• 2008

Even in a single day, an enormous amount of content including digital videos, posts, photographs, and wikis are generated on the web. It's getting more difficult to recommend to a user what he/she prefers among these contents because of the difficulty of automatically grasping of content's meanings. CF (Collaborative Filtering) is one of useful methods to recommend proper content to a user under these situations because the filtering process is only based on historical information about whether or not a target user has preferred an item before. Collaborative Tagging is the process that allows many users to annotate content with descriptive tags. Recommendation using tags can partially improve, such as the limitations of CF, the sparsity and cold-start problem. In this research, a CF method with user-created tags is proposed. Collaborative tagging is employed to grasp and filter users' preferences for items. Empirical demonstrations using real dataset from del.icio.us show that our algorithm obtains improved performance, compared with existing works.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.6 no.1
• /
• pp.143-149
• /
• 2010

In ubiquitous environment, information retrieval using collaborative filtering is a popular technique for reducing information overload. Collaborative filtering systems can produce personal recommendations by computing the similarity between your preference and the one of other people. We integrate the collaboration filtering method and context-aware information retrieval method. The proposed method enables to find some relevant information to specific user's contexts. It aims to makes more effective information retrieval to the users. The proposed method is conceptually comprised of two main tasks. The first task is to tag context tags by automatic tagging technique. The second task is to recommend items for each user's contexts integrating collaborative filtering and information retrieval. We describe a new integration method algorithm and then present a u-commerce application prototype.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.1
• /
• pp.446-465
• /
• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.18 no.2
• /
• pp.141-159
• /
• 2007

The purpose of this study was to try to conceptualize the Folksonomy, so called collaborative taggng or bookmarking and suggest its application on the Web information services. This paper explores how folksonomies could be used in web information services to enable end users to manage personal information spaces, get helped existing controlled vocabularies, and create and share their interests in online communities. Traditional classification system and philosophical issues on Folksonomy were reviewed in this paper in the context of internet based information and its services. The benefits and shortcomings of folksonomies are discussed. Some of the customizable features in existing library catalogue systems are reviewed to suggest other applicable features for web information services.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.8 no.12
• /
• pp.1911-1916
• /
• 2013

One significant challenge that arises in social tagging systems is the rapid increase in the number and diversity of the tags. As opposed to structured annotation systems, tags provide users an unstructured, open-ended mechanism to annotate and organize web-content. In this paper, we propose a scheme for URL recommendation that is based on a folksonomy which is comprised of user-defined tags, URL-keywords and the category folder name as the major element. This scheme will be further improved and implemented on a browser extension that recommends to users the best way to classify a particular URL.

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

• Database Research
• /
• v.34 no.3
• /
• pp.99-123
• /
• 2018

Due to the ever expanding nature of the Web, separating more valuable information from the noisy data is getting more important. Although recommendation systems are widely used for addressing the information overloading issue, their performance does not seem meaningfully improved in currently suggested approaches. Hence, to investigate the issues, this study discusses different characteristics of popular, existing recommendation approaches, and proposes a new profiling technique that uses collaborative tags and test whether it successfully compensates the limitations of the existing approaches. In addition, the study also empirically evaluates rating/tagging patterns of users in various recommendation approaches, which include the proposed approach, to learn whether those patterns can be used as effective cues for improving the recommendations accuracy. Through the sensitivity analyses, this study also suggests the potential associated with a single recommendation system that applies multiple approaches for different users or items depending upon the types and contexts of recommendations.

• Plant Breeding and Biotechnology
• /
• v.6 no.4
• /
• pp.313-320
• /
• 2018

Rice is the staple food of more than 50% of the world population. Cultivated rice has the AA genome (diploid, 2n = 24) and small genome size of only 430 megabase (haploid genome). As the sequencing of rice genome was completed by the International Rice Genome Sequencing Project (IRGSP), many researchers in the world have been working to explore the gene function on rice genome. Insertional mutagenesis has been a powerful strategy for assessing gene function. In maize, well characterized transposable elements have traditionally been used to clone genes for which only phenotypic information is available. In rice endogenous mobile elements such as MITE and Tos have been used to generate gene-tagged populations. To date T-DNA and maize transposable element systems have been utilized as main insertional mutagens in rice. The Ac/Ds system offers the advantage of generating new mutants by secondary transposition from a single tagged gene. To enhance the efficiency of gene detection, advanced gene-tagging systems (i.e. activation, gene or enhancer trap) have been employed for functional genomic studies in rice. Internationally, there have been many projects to develop large scales of insertional mutagenized populations and databases of insertion sites has been established. Ultimate goals of these projects are to supply genetic materials and informations essential for functional analysis of rice genes and for breeding using agronomically important genes. In this report, we summarize the current status of Ac/Ds-mediated gene tagging systems that has been conducted by collaborative works in Korea.

• Journal of Plant Biotechnology
• /
• v.37 no.2
• /
• pp.125-132
• /
• 2010

Rice is the staple food of more than 50% of the worlds population. Cultivated rice has the AA genome (diploid, 2n=24) and small genome size of only 430 megabase (haploid genome). As the sequencing of rice genome was completed by the International Rice Genome Sequencing Project (IRGSP), many researchers in the world have been working to explore the gene function on rice genome. Insertional mutagenesis has been a powerful strategy for assessing gene function. In maize, well characterized transposable elements have traditionally been used to clone genes for which only phenotypic information is available. In rice endogenous mobile elements such as MITE and Tos (Hirochika. 1997) have been used to generate gene-tagged populations. To date T-DNA and maize transposable element systems has been utilized as main insertional mutagens in rice. A main drawback of a T-DNA scheme is that Agrobacteria-mediated transformation in rice requires extensive facilities, time, and labor. In contrast, the Ac/Ds system offers the advantage of generating new mutants by secondary transposition from a single tagged gene. Revertants can be utilized to correlate phenotype with genotype. To enhance the efficiency of gene detection, advanced gene-tagging systems (i.e. activation, gene or enhancer trap) have been employed for functional genomic studies in rice. Internationally, there have been many projects to develop large scales of insertionally mutagenized populations and databases of insertion sites has been established. Ultimate goals of these projects are to supply genetic materials and informations essential for functional analysis of rice genes and for breeding using agronomically important genes. In this report, we summarize the current status of Ac/Ds-mediated gene tagging systems that has been launched by collaborative works from 2001 in Korea.