A Federated Recommender System for Online Learning Environments

From e-commerce to social networking sites, recommender systems are gaining more and more interest. They provide connections, news, resources, or products of interest. This paper presents a federated recommender system, which exploits data from different online learning platforms and delivers personalized recommendation. The underlying educational objective is to enable academic institutions to provide a Web 2.0 dashboard bringing together open resources from the Cloud and proprietary content from in-house learning management systems. The paper describes the main aspects of the federated recommender system, including its adopted architecture, the common data model used to harvest the different learning platforms, the recommendation algorithm, as well as the recommendation display widget.


Published in:
Advances in Web-Based Learning - ICWL 2012, 89-98
Presented at:
11th International Conference on Web-Based Learning, Sinaia, Romania, September 2-4, 2012
Year:
2012
Publisher:
Berlin, Springer Berlin Heidelberg
ISBN:
978-3-642-33642-3
Keywords:
Laboratories:




 Record created 2012-11-25, last modified 2018-01-28

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