• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.11-17
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• 2018

In this paper, we propose how to improve the lack of diversity in collaborative filtering, using tag scores contained in items rather than ratings of items. Collaborative filtering has excellent performance among recommendation system, but it is evaluated as lacking diversity. In order to solve this problem, this paper proposes a method for supplementing diversity lacking in collaborative filtering by using tags. By using tags that can be used universally without using the characteristics of specific articles in a recommendation system, The proposed method can be used.

• 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.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.157-177
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• 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 the Korea Society of Computer and Information
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• v.17 no.11
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• pp.133-140
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• 2012

In this paper, we propose a music recommendation method considering users' tags by collaborative tagging in a social music site. Since collaborative tagging allows a user to add keywords chosen by himself to web resources, it provides users' preference about the web resources concretely. In particular, emotional tags which represent human's emotion contain users' musical preference more directly than factual tags which represent facts such as musical genre and artists. Therefore, to classify the tags into the emotional tags and the factual tags and to assign weighted values to the emotional tags, a tag ontology called UniTag is developed. After preprocessing the tags, the weighted tags are used to create user profiles, and the music recommendation algorithm is executed based on the profiles. To evaluate the proposed method, a conventional playcount-based recommendation, an unweighted tag-based recommendation, and an weighted tag-based recommendation are executed. Our experimental results show that the weighted tag-based recommendation outperforms other two approaches in terms of precision.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.193-201
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• 2009

The use of tagging to describes web documents in the form of keyword has experienced rising popularity among various web services. Tagging also plays an important role in collaborative bookmarking services which can be regarded as an online favorite bookmark service. Tags which are created by users make it easier to search other users' bookmarks as well as user's own bookmarks. In this paper we analyze usage patterns of collaborative tagging for exploring factors influencing the number of tags in web documents and users. We discovered that user's characteristics have more effect on the tags than the web documents' characteristics. Moreover, leading users contribute to make a variety of tag than following users. Our study implies that more knowledge can be created through the incentives for leading user in order to improve the service quality of tagging service.

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

• Journal of the Korean Society for Library and Information Science
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• v.43 no.4
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• pp.327-351
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• 2009

This study analyzed the characteristics and functions of tags in academic libraries and commercial sites. Tags in tag clouds from 6 different sites were collected in terms of tag popularity and importance. The gathered tags were analyzed in terms of tagging purpose, the tagging function, and the physical characteristics of tags. The results can be used to apply the tagging service to academic digital libraries.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.29-37
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• 2011

Recently, many user-participating web services have been used widely as the evolution of internet web technology has rapidly been developed. Users share various content and opinion on line using a site like ‘Social bookmarking.’ Users can share others’ bookmarking history and create tags while bookmarking web sites; we call it collaborative tagging. In this paper, we studied empirical analysis for widely used social bookmarking and collaborative tagging which the result shows minority of users is actively using the bookmarking and a few sites and tags are used by majority of the users. 24% users tagged 80%, 75% sites and 81% tags were tagged below than 3 times. Types of bookmarking activities were found different by users and early appointed tags get more frequency by majority. We also identified relative proportions of tags on certain sites are becoming convergence gradually. We expect the result of this paper will give opportunities to help further developing social bookmarking system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.82-88
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• 2017

Recently recommendation methods need personalization and diversity as well as accuracy whereas the traditional researches have been mainly focused on the accuracy of recommendation in terms of quality. The diversity of recommendation is also important to people in terms of quantity in addition to quality since people's desire for content consumption have been stronger rapidly than past. In this paper, we pay attention to similarity of data gathered simultaneously among different types of contents. With this motivation, we propose an enhanced recommendation method using correlation analysis with considering data similarity between two types of contents which are movie and music. Specifically, we regard folksonomy tags for music as correlated data of genres for movie even though they are different attributes depend on their contents. That is, we make result of new recommendation movie items through mapping music folksonomy tags to movie genres in addition to the recommendation items from the typical collaborative filtering. We evaluate effectiveness of our method by experiments with real data set. As the result of experimentation, we found that the diversity of recommendation could be extended by considering data similarity between music contents and movie contents.

• Journal of the Korean Society for Library and Information Science
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• v.42 no.4
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• pp.463-480
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• 2008

This study was to review the possible characteristics and patterns that occur when comparing control language constructing guidelines, by analyzing the formal characteristics of folksonomy tags in university libraries. Based on subjected tags at university libraries for a period of 6 months the structure and form of folksonomy was examined. The object tags were analyzed based on the thesaurus development guidelines. The results for this research will provide baseline data for the use of folksonomy tag applications in digital libraries.