Recommending Multimedia Educational Resources on the MOVING Platform

The MOVING platform includes a huge amount of heterogeneous educational resources, such as documents, videos, and social media posts. We show how the MOVING recommender system can support users in dealing with such a massive information flow by leveraging semantic profiling. The HCF-IDF model exploits a thesaurus or ontology to represents users and documents and it is used to recommend educational resources based on users’ search history. We describe how the recommender is implemented how it is applied to the MOVING platform to deal with the huge amount of resources stored in the platform, their variety and the increasing number of users.

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