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A content recommender system or a recommendation system represents a subclassof information filtering systems which seeks to predict the user preferences,i.e. the content that would be most likely positively "rated" by the user.Nowadays, the recommender systems of OpenCourseWare (OCW) platforms typicallygenerate a list of recommendations in one of two ways, i.e. through thecontent-based filtering, or user-based collaborative filtering (CF). In thispaper, the conceptual idea of the content recommendation module was provided,which is capable of proposing the related decks (presentations, educationalmaterial, etc.) to the user having in mind past user activities, preferences,type and content similarity, etc. It particularly analyses suitable techniquesfor implementation of the user-based CF approach and user-related features thatare relevant for the content evaluation. The proposed approach also envisages ahybrid recommendation system as a combination of user-based and content-basedapproaches in order to provide a holistic and efficient solution for contentrecommendation. Finally, for evaluation and testing purposes, a designatedcontent recommendation module was implemented as part of the SlideWikiauthoring OCW platform.

User-based collaborative filtering approach for content recommendation in OpenCourseWare platforms