Personal recommender systems for learners in lifelong learning networks: requirements, techniques and model

This article argues for the need of personal recommender systems in lifelong learning networks that provide learners advice on suitable learning activities to follow. Existing recommender systems and recommendation techniques used for consumer products and other contexts are assessed on their suitability for providing navigation support in a learning network. Similarities and differences are translated into specific demands for learning and specific requirements for recommendation techniques. We propose a combination of memory-based recommendation techniques that appear suitable to realize personalized recommendation on learning activities in the context of e-learning. An initial model for the design of such systems in learning networks and a roadmap for their further development are presented.