Purpose – A good recommender system helps users find items of interest on the web and can provide recommendations based on user preferences. In contrast to automatic technology-generated recommender systems, this paper aims to use dynamic expert groups that are automatically formed to recommend domain-specific documents for general users. In addition, it aims to test several effectiveness measu...

As the information on the Web grows, the need of recommender systems to ease user navigations becomes evident. There exist many approaches of learning for Web usage based recommender systems. In this study, we apply and compare some of the methods of usage pattern discovery, like simple k-means clustering algorithm, fuzzy relational subtractive clustering algorithm, fuzzy mean field annealing c...

This article presents research on personal recommender systems for lifelong learning. The personal recommender systems supports lifelong learners in Learning Networks. A first version was evaluated in an experiment during an Introduction Psychology course of the Psychology Department at the Open University of the Nederlands. The learning activities of the psychology course and the personal reco...

Abstract The following article deals with the topic of discrimination “by” a recommender system. Several reasons can create discriminating recommendations, especially lack diversity in training data, bias data or errors underlying modelling algorithm. legal frame is still not sufficient to nudge developers users effectively avoid those discriminations, protection law as enshrined EU General Dat...

Recently, recommender systems have been introduced to predict user preferences for products or services. In order to seek better prediction techniques, data owners of recommender systems such as Netflix sometimes make their customers’ reviews available to the public, which raises serious privacy concerns. With only a small amount of knowledge about individuals and their ratings to some items in...