• Proceedings of the Korean Society for Information Management Conference
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• 2006.08a
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• pp.63-70
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• 2006

이용자중심의 정보기술이 발전하면서 웹 2.0이라는 개념이 등장하고 이용자가 정보를 조직하는 사회적 태깅 방식의 웹사이트가 확산되고 있다. Connotea와 CiteULike는 웹 2.0이라는 개념의 일부인 사회적 태깅 방식을 학술영역에 본격적으로 도입한 최초의 서비스로서, 과거의 통제어휘 위주의 디렉토리 분류체계를 벗어나 이용자들이 참여하여 제공한 정보와 협력하여 부여한 키워드를 중심으로 자유롭게 콘텐트를 분류하고 관리하는 정보공유 포털서비스이다. 이 글에서는 이와 같은 이용자 참여를 유도하는 정보서비스동향을 개관하고 Connotea와 CiteULike의 두 서비스를 비교해보았다.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.1-21
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• 2015

This paper aims to assess the feasibility of a new and less-focused type of online sociability (the watching network) as a useful information source for personalized recommendations. In this paper, we recommend scientific articles of interests by using the shared interests between target users and their watching connections. Our recommendations are based on one typical social bookmarking system, CiteULike. The watching network-based recommendations, which use a much smaller size of user data, produces suggestions that are as good as the conventional Collaborative Filtering technique. The results demonstrate that the watching network is a useful information source and a feasible foundation for information personalization. Furthermore, the watching network is substitutable for anonymous peers of the Collaborative Filtering recommendations. This study shows the expandability of social network-based recommendations to the new type of online social networks.

• Journal of the Korean Society for information Management
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• v.33 no.1
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• pp.317-336
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• 2016

Nowadays users spend more time and effort to find what they want because of information overload. To solve the problem, scientific article recommendation system analyse users' needs and recommend them proper articles. However, most of the scientific article recommendation systems neglected the core part, user profile. Therefore, in this paper, instead of mean which applied in user profile in previous studies, New TPIPF (Topic Proportion-Inverse Paper Frequency) was applied to scientific article recommendation system. Moreover, the accuracy of two scientific article recommendation systems with above different methods was compared with experiments of public dataset from online reference manager, CiteULike. As a result, the proposed scientific article recommendation system with TPIPF was proven to be better.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.1
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• pp.189-210
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• 2011

This study aims to explore how users are tagging in order to utilize a folksonomy and whether they understand the social and interactive aspects of tagging in three different folksonomic systems, Connotea (www.connotea.org), Delicious(http://delicious.com), and CiteULike(www.citeulike.org). The study uses internet questionnaires, qualitative diary studies, and follow-up interviews to understand twelve participants' tagging activities associated with folksonomic interactions. The flow charts developed from the twelve participants showed that tagging was a quite complex process, in which each tagging activity was interconnected, and a variety of folksonomic system features were employed. Three main tagging activities involved in the tagging processes have been identified: item selection, tag assignment, and tag searching and discovery. During the tag assignment, participants would describe their tagging motivations related to various types of tags. Their perception of the usefulness of types of tags was different when their purpose was for social sharing rather than personal information management. While tagging, participants recognized the social potential of a folksonomic system and used interactive aspects of tagging via various features of the folksonomic system. It is hoped that this empirical study will provide insight into theoretical and practical issues regarding users' perceptions and use of folksonomy in accessing, sharing, and navigating internet resources.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.42-44
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• 2012

본 논문에서는 개인화 검색 시 사용자의 단일 프로파일이 개인의 다양한 정보 요구를 만족시키지 못하는 문제를 개선하고자, 질의어에 따라 프로파일을 조정하는 방법을 제안한다. 특히, 제안하는 방법은 북마크 데이터로부터 질의어에 관해 사용자가 중요하게 생각하는 단어들을 추출하여 프로파일을 조정하는데 활용한다. 유명 북마크 서비스인 CiteULike의 데이터를 활용한 실험에서, 제안하는 방법이 단일 프로파일에 기반한 기존의 방법보다 더 뛰어난 개인화 검색 결과를 제공함을 확인할 수 있었다.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1242-1250
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• 2022

When recommending cold items that do not have user-item interactions to users, even we adopt state-of-the-arts algorithms, the predicted information of cold items tends to have lower accuracy compared to warm items which have enough user-item interactions. The lack of information makes for recommender systems to recommend monotonic items which have a few top popular contents matched to user preferences. As a result, under-diversified items have a negative impact on not only recommendation diversity but also on recommendation accuracy when recommending cold items. To address the problem, we adopt a diversification algorithm which tries to make distributions of accumulated contents embedding of the two items groups, recommended items and the items in the target user's already interacted items, similar. Evaluation on a real world data set CiteULike shows that the proposed method improves not only the diversity but also the accuracy of cold item recommendation.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1673-1681
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• 2021

Item cold start is a well studied problem in the research field of recommender systems. Still, many existing collaborative filters cannot recommend items accurately when only a few user-item interaction data are available for newly introduced items (Cold items). We propose a interaction feature prediction method to mitigate item cold start problem. The proposed method predicts the interaction features that collaborative filters can calculate for the cold items. For prediction, in addition to content features of the cold-items used by state-of-the-art methods, our method exploits the interaction features of k-nearest content neighbors of the cold-items. An attention network is adopted to extract appropriate information from the interaction features of the neighbors by examining the contents feature similarity between the cold-item and its neighbors. Our evaluation on a real dataset CiteULike shows that the proposed method outperforms state-of-the-art methods 0.027 in Recall@20 metric and 0.023 in NDCG@20 metric.

• Journal of Information Science Theory and Practice
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• v.7 no.1
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• pp.29-38
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• 2019

Information retrieval is the challenge of the Web 2.0 world. The experiment of knowledge organisation in the context of abundant information available from various sources proves a major hurdle in obtaining information retrieval with greater precision and recall. The fast-changing landscape of information organisation through social networking sites at a personal level creates a world of opportunities for data scientists and also library professionals to assimilate the social data with expert created data. Thus, folksonomies or social tags play a vital role in information organisation and retrieval. The comparison of these user-created tags with expert-created index terms, author keywords and title words, will throw light on the differentiation between these sets of data. Such comparative studies show revelation of a new set of terms to enhance subject access and reflect the extent of similarity between user-generated tags and other set of terms. The CiteULike tags extracted from 5,150 scholarly journal articles in marine science were compared with corresponding Aquatic Science and Fisheries Abstracts descriptors, author keywords, and title terms. The Jaccard similarity coefficient method was employed to compare the social tags with the above mentioned wordsets, and results proved the presence of user-generated keywords in Aquatic Science and Fisheries Abstracts descriptors, author keywords, and title words. While using information retrieval techniques like stemmer and lemmatization, the results were found to enhance keywords to subject access.