Abstract
Tags are terms commonly used in collaborative media systems like Flickr, Youtube and Picasa to classify a subject, image, video, music or any related content. Despite its popularity, tagging is a repetitive task and that may affect the quality and reuse of tags in collaborative systems. In this paper we use a model-based tag recommendation approach to perform an experiment and analyze the vocabulary homogeneity of queries (tags provided by users), the recommended tags and their reuse. Results show that the use of recommendation improves the quality and reuse of tags. Furthermore, based on users attribution behavior, we conclude with a proposal for personalized tag recommendation.
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de C.A. Ziesemer, A., de Oliveira, J.B.S. (2013). Keep Querying and Tag on: Collaborative Folksonomy Using Model-Based Recommendation. In: Antunes, P., Gerosa, M.A., Sylvester, A., Vassileva, J., de Vreede, GJ. (eds) Collaboration and Technology. CRIWG 2013. Lecture Notes in Computer Science, vol 8224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41347-6_2
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DOI: https://doi.org/10.1007/978-3-642-41347-6_2
Publisher Name: Springer, Berlin, Heidelberg
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