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Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions

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Abstract

Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.

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Authors and Affiliations

  1. Higher School of Professional Business Studies, University of Novi Sad, Novi Sad, Serbia

    Aleksandra Klasnja Milicevic

  2. Information Systems and Machine Learning Lab, University of Hildesheim, Hildesheim, Germany

    Alexandros Nanopoulos

  3. Faculty of Science, Department of Mathematics and Informatics, University of Novi Sad, Novi Sad, Serbia

    Mirjana Ivanovic

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  1. Aleksandra Klasnja Milicevic
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  2. Alexandros Nanopoulos
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  3. Mirjana Ivanovic
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Correspondence to Aleksandra Klasnja Milicevic.

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Milicevic, A.K., Nanopoulos, A. & Ivanovic, M. Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions. Artif Intell Rev 33, 187–209 (2010). https://doi.org/10.1007/s10462-009-9153-2

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