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Folksonomy and Tag-Based Recommender Systems in E-Learning Environments

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E-Learning Systems

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 112))

Abstract

Collaborative tagging is technique, highly employed in different domains, which is used for automatic analysis of users’ preferences and recommendations. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing reputation of the collaborative tagging systems, tags could be interesting and provide useful information to enhance algorithms for recommender systems. Besides helping user to organize his/her personal collections, a tag also can be regarded as a user’s personal opinion expression, while tagging can be considered as implicit rating or voting on the tagged information resources or items. The overview, presented in this chapter includes descriptions of content-based recommender systems, collaborative filtering systems, hybrid approach, memory-based and model-based algorithms, features of collaborative tagging that are generally attributed to their success and popularity, as well as a model for tagging activities and tag-based recommender systems.

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Notes

  1. 1.

    http://www.flickr.com.

  2. 2.

    http://www.del.icio.us.

  3. 3.

    http://www.citeulike.org.

  4. 4.

    http://www.connotea.org.

  5. 5.

    http://www.43things.com.

  6. 6.

    http://www.technorati.com.

  7. 7.

    http://www.upcoming.yahoo.com.

  8. 8.

    http://www.youtube.com.

  9. 9.

    http://www.espgame.org.

  10. 10.

    http://podcasts.yahoo.com.

  11. 11.

    http://myweb.yahoo.com.

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Authors and Affiliations

  1. Faculty of Sciences, Department of Mathematics and Informatics, University of Novi Sad, Novi Sad, Serbia

    Aleksandra Klašnja-Milićević, Mirjana Ivanović & Zoran Budimac

  2. Department of Computer Science and Engineering, University of Gothenburg/Chalmers, University of Technology, Gothenburg, Sweden

    Boban Vesin

  3. Bournemouth University, Fern Barrow, Poole, UK

    Lakhmi C. Jain

  4. University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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  1. Aleksandra Klašnja-Milićević
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  2. Boban Vesin
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  3. Mirjana Ivanović
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Correspondence to Aleksandra Klašnja-Milićević .

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Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z., Jain, L.C. (2017). Folksonomy and Tag-Based Recommender Systems in E-Learning Environments. In: E-Learning Systems. Intelligent Systems Reference Library, vol 112. Springer, Cham. https://doi.org/10.1007/978-3-319-41163-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-41163-7_7

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