Recommendations for learners are different: Applying memory-based recommender system techniques to lifelong learning

Abstract

This article argues why personal recommender systems in technology-enhanced learning have to be adjusted to the specific character of learning. Personal recommender systems are strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system from one context and transfer it to another context or domain. The article describes a number of distinct differences for personalized recommendation to consumers in contrast to recommendations to learners.Similarities and differences are translated into specific demands for learning and specific requirements for personal recommendation systems. Therefore it analyses memory-based recommendation techniques for their usefulness to provide pedagogically reasonable recommendations to learners. recommender systems, collaborative filtering, content-based recommendation, user profiling.