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2017 | OriginalPaper | Buchkapitel

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

verfasst von : Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain

Erschienen in: E-Learning Systems

Verlag: Springer International Publishing

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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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Titel: Folksonomy and Tag-Based Recommender Systems in E-Learning Environments
verfasst von: Aleksandra Klašnja-Milićević
Boban Vesin
Mirjana Ivanović
Zoran Budimac
Lakhmi C. Jain
Verlag: Springer International Publishing
Buch: E-Learning Systems
Print ISBN: 978-3-319-41161-3

Electronic ISBN: 978-3-319-41163-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-41163-7_7

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