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Erschienen in:
Introduction to E-Learning Systems Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

6. 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

Recommender system can be defined as a platform for providing recommendations to users based on their personal likes and dislikes. These systems use a specific type of information filtering technique that attempt to recommend information items (movies, music, books, news, Web pages, learning objects, and so on.) to the user. Recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take a recommendation strategy from one context and transfer it to another context or domain. Personalized recommendation can help learners to overcome the information overload problem, by recommending learning resources according to learners’ habits and level of knowledge. The first challenge for designing a recommender component for e-learning systems is to define the learners and the purpose of the specific context or domain in a proper way. This chapter provides an overview of techniques for recommender systems, folksonomy and tag-based recommendation to assist the reader in understanding the material which follows in subsequent chapters.

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Vorheriges Kapitel Agents in E-Learning Environments
Nächstes Kapitel Folksonomy and Tag-Based Recommender Systems in E-Learning Environments
Fußnoten
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Metadaten
Titel
Recommender Systems in E-Learning Environments
verfasst von
Aleksandra Klašnja-Milićević
Boban Vesin
Mirjana Ivanović
Zoran Budimac
Lakhmi C. Jain
Copyright-Jahr
2017
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_6

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