Recommender Systems for Technology-Supported Learning

Recommender systems have become an important research area since the emergence of the first research paper on collaborative filtering in the mid-1990s. In general, recommender systems directly help users to select content, products, or services by aggregating and analysing historical data including suggestions from other users, and turning them into predictions of users’ possible future preferences. Recommender systems combine ideas from user profiling, information filtering, data mining, machine learning and social networking to provide personalized and meaningful recommendations. For example, while standard search engines are very likely to generate the same results to the same search queries entering from different users, recommender systems are able to generate results that are personalized taking into account the individual user’s profile.