Academic literature on the topic 'CiteULike'

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Journal articles on the topic "CiteULike"

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McMullen, Anthony. "CiteULike." Public Services Quarterly 6, no. 4 (2010): 368–69. http://dx.doi.org/10.1080/15228951003772595.

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Boughareb, Djalila, Abdennour Khobizi, Rima Boughareb, Nadir Farah, and Hamid Seridi. "A Graph-Based Tag Recommendation for Just Abstracted Scientific Articles Tagging." International Journal of Cooperative Information Systems 29, no. 03 (2020): 2050004. http://dx.doi.org/10.1142/s0218843020500045.

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Tags, when properly assigned to limited access papers, help users to estimate their relevance. This paper introduces a new approach for the selection of relevant tags as well as a recommendation for scientific papers tagging. The approach defines the relatedness between the tags attributed by users and the concepts extracted from the available sections of scientific papers based on statistical, structural and semantic aspects. Two different term-based graphs ([Formula: see text]-graph and [Formula: see text]-graph) were generated whose vertices indicate the terms and the edges represent the re
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Us Saeed, A., M. T. Afzal, A. Latif, and K. Tochtermann. "DISSEMINATING KNOWLEDGE THROUGH TAGS: RECOMMENDING TAGS FOR SCIENTIFIC RESOURCES." Journal of IT in Asia 3, no. 1 (2016): 39–58. http://dx.doi.org/10.33736/jita.33.2010.

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Knowledge diffusion has prime importance for generation of new knowledge. The creation of new knowledge is not possible without referring or consulting the past work. Two very important potential flows related to knowledge diffusion can be observed in the common practice of researchers. First, in scientific research knowledge diffusion estimation using citation counts is generally used to establish the value of knowledge which inflates citations. Second the researchers use cited work to search the connected and related resources. Recently the social and collaborative phenomena termed as Web 2.
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Ma, Jin. "The sustainability and stabilization of tag vocabulary in CiteULike." Online Information Review 36, no. 5 (2012): 655–74. http://dx.doi.org/10.1108/14684521211275966.

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Yang, Jing, Jun Wang, and Mengyang Gao. "Community Evolution Analysis Driven by Tag Events: The Special Perspective of New Tags." Mathematics 11, no. 6 (2023): 1361. http://dx.doi.org/10.3390/math11061361.

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The type, quantity, and scale of social-tagging systems have grown constantly in recent years as users’ interest increases. Tags have important reference value in the study of networked communities since they typically represent user preference. This paper aims to examine how a tagging community evolves and to check the impact of new tags on evolution. Therefore, we proposed an improved evolution model for tag communities where tags constantly accumulate without withdrawal. Based on the model, we conducted an evolution analysis on three different tag communities with the datasets generated fro
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Smith, Felicia A. "CiteULike2007307Richard Cameron. CiteULike. 2004‐. Last visited April 2007 URL: www.citeulike.org Gratis." Reference Reviews 21, no. 7 (2007): 7–8. http://dx.doi.org/10.1108/09504120710821497.

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Hughes, Emma. "Personal Publications Lists Serve as a Reliable Calibration Parameter to Compare Coverage in Academic Citation Databases with Scientific Social Media." Evidence Based Library and Information Practice 12, no. 1 (2017): 125. http://dx.doi.org/10.18438/b8md0g.

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A Review of:
 Hilbert, F., Barth, J., Gremm, J., Gros, D., Haiter, J., Henkel, M., Reinhardt, W., & Stock, W.G. (2015). Coverage of academic citation databases compared with coverage of scientific social media: personal publication lists as calibration parameters. Online Information Review 39(2): 255-264. http://dx.doi.org/10.1108/OIR-07-2014-0159
 
 Abstract
 
 Objective – The purpose of this study was to explore coverage rates of information science publications in academic citation databases and scientific social media using a new method of personal publication
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Kipp, Margaret E. I. "Tagging of Biomedical Articles on CiteULike: A Comparison of User, Author and Professional Indexing." KNOWLEDGE ORGANIZATION 38, no. 3 (2011): 245–61. http://dx.doi.org/10.5771/0943-7444-2011-3-245.

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Lee, Danielle H., and Peter Brusilovsky. "How to measure information similarity in online social networks: A case study of Citeulike." Information Sciences 418-419 (December 2017): 46–60. http://dx.doi.org/10.1016/j.ins.2017.07.034.

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Kipp, Margaret E. I. "User, Author and Professional Indexing in Context: An Exploration of Tagging Practices on CiteULike / Le contexte de l'indexation des usagers, des créateurs et des professionnels : une exploration des pratiques d'étiquetage social sur CiteULike." Canadian Journal of Information and Library Science 35, no. 1 (2011): 17–48. http://dx.doi.org/10.1353/ils.2011.0008.

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Book chapters on the topic "CiteULike"

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"Beyond Scientometrics." In Scholarly Content and Its Evolution by Scientometric Indicators. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5945-0.ch007.

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Numerous websites are currently being used by researchers for sharing and disseminating research, some of which are CiteULike, BibSonomy, Connotea, Mendeley, ResearchGate, etc. For measuring this data, scientists create alternative indicators related to traditional indicators like bibliometric indicators, scientometric indicators. The main purpose of these indicators is that with such huge amount of information available, some specific tools and techniques are required to filter and evaluate the research outcomes. These indicators reveal the societal and unknown impact of the work that traditi
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Rodriguez, Abel, and Radhakrishna Vuppala. "A Recommendation System for Scientific Papers through Bayesian Nonparametric Hybrid Filtering." In Advances in Data Mining and Database Management. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5063-3.ch002.

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Recommender systems have become an important area of research with numerous applications on e-commerce. This chapter introduces a joint statistical model for user preferences and item features that can serve as the basis for a recommendation about recently published scientific papers. The model is constructed using ideas from the literature on Bayesian nonparametric mixture modeling. More specifically, user preferences are modeled using an Infinite Relational Model (IRM) in which both users and items are independently partitioned into homogeneous groups, while item features are modeled using a
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Conference papers on the topic "CiteULike"

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Bogers, Toine, and Antal van den Bosch. "Recommending scientific articles using citeulike." In the 2008 ACM conference. ACM Press, 2008. http://dx.doi.org/10.1145/1454008.1454053.

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Noel, Sylvie, and Russell Beale. "Sharing Vocabularies: Tag Usage in CiteULike." In People and Computers XXII Culture, Creativity, Interaction. BCS Learning & Development, 2008. http://dx.doi.org/10.14236/ewic/hci2008.36.

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Jomsri, P., S. Sanguansintukul, and W. Choochaiwattana. "A comparison of search engine using “tag title and abstract” with CiteULike — An initial evaluation." In 2009 4th International Conference for Internet Technology and Secured Transactions (ICITST 2009). IEEE, 2009. http://dx.doi.org/10.1109/icitst.2009.5402563.

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