Special Issue on Advances in Recommender Systems

Recent advances in electronic media and computer networks have allowed the creation of large and distributed repositories of information. However, the immediate availability of extensive resources for use by broad classes of computer users gives rise to new challenges in everyday life. These challenges arise from the fact that users cannot exploit available resources effectively when the amount of information requires prohibitively long user time spent on acquaintance with and comprehension of the information content. Thus, the risk of information overload of users imposes new requirements on the software systems that handle the information. Such systems are called Recommender Systems and attempt to provide information in a way that will be most appropriate and valuable to its users and prevent them from being overwhelmed by huge amounts of information that, in the absence of recommender systems, they should browse or examine. The previous give rise to a need for successfully providing efficient learning algorithms, software applications, and evaluation methodologies in order to achieve high quality in recommendation services offered to the people. The aim of this special issue of Intelligent Decision Technologies is to shed light on these needs and to broaden our understanding of past and potential approaches to recommender systems and technologies. Thus, potential authors are invited to submit contributions related to the field of recommender systems. Submissions may be of a theoretical nature, may present recommender systems and applications developed by the authors, or may comparatively evaluate alternative methodologies and algorithms that are used within recommender systems. Topics of interest