Focus on : 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. In general, two recommendation techniques have come to dominate: content-based filtering (CBF) and collaborative filtering (CF). In general, the first approach recommends to a user items whose content is similar to content that the user has previously viewed or selected. This has been used mainly in the context of recommending items such as books, movies, web pages, news, etc. for which informative content descriptors do exist. To accurately represent and update the features of the items is expensive, time consuming, error-prone and highly subjective. On the other hand, collaborative filtering collects information about user’s rated items and makes recommendations based on items which were highly rated by users with similar profile. CF algorithms generally compute the overall similarity between users, and use that as a weight when making recommendations. Therefore, the CF techniques can be applied to virtually any kind of items and promise to scale well to large item bases becoming the most widely used approach for building online recommender systems. Finally, some systems combine both content and collaborative filtering approaches to make recommendations. Journal of e-Learning and Knowledge Society Je-LKS