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Defining Personalized Learning Views of Relevant Learning Objects in a Collaborative Bookmark Management System
Abstract
In this chapter, we introduce how to use a Web-based hybrid recommender system developed with a collaborative bookmark management system approach. The system combines content analysis and the development of virtual clusters of students and educational sources. It provides facilitation in the use of a huge amount of digital information stored in a distributed learning environment on the basis of the student’s personal requirements and interests. By adopting a hybrid approach, the system is able to effectively filter relevant resources from a wide heterogeneous environment like the Web, taking advantage of the common interests of the users and also maintaining the benefits provided by content analysis. The basic idea is to appropriately help students classifying domain-specific information found on the Web and saved as bookmarks, to recommend these documents to other students with similar interests, and to notify users periodically about new, potentially interesting documents. Documents are represented using metadata model.
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