The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender sys-tem requires more than a clever general-purpose algorithm. It requires an in-depth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human-computer interaction problem, where a computerized system supports users in information search or decision-making contexts. This special issue contains a selection of papers reflecting this multi-faceted nature of the problem and puts open research challenges in recommender systems to the fore-front. It features articles on the latest learning technology, reflects on the human-computer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology.
Team PhyPA: Brain-Computer Interfacing for Everyday Human-Computer Interaction
