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Recommender Systems are omnipresent in our digital life. Most notably, various media platforms guide us in selecting videos, but recommender systems are also used for more serious goals, such as news selection, political orientation and work decisions. As argued in this survey and position article, the paradigm of recommendation-based feeds has changed user behaviour from active decision making to rather passively following recommendations and accepting possibly suboptimal choices that are deemed "good enough". We provide a historic overview of media selection, discuss assumptions and goals of recommender systems and identify their shortcomings, based on existing literature. Then, the perspective changes to hypertext as a paradigm for structuring information and active decision making. To illustrate the relevance and importance of active decision making, we present a use case in the field of TV or media selection and (as a proof of concept) carried over to another application domain: maintenance in industry. In the discussion section, we focus on categorising these actions on a spectrum of "system-1" (fast and automated) tasks and "system-2" (critical thinking) tasks. Further, we argue how users can profit from tools that combine active (spatial) structuring and categorising with automatic recommendations, for professional tasks as well as private, leisure activities. [ABSTRACT FROM AUTHOR]

Historically, there has been a tendency to consider hypertext as a type of system, perhaps characterised by provision of links or other structure to users. In this article, we consider hypertext as a method of inquiry, a way of viewing arbitrary systems. In this view, what are traditionally called "navigational hypertext systems" might be considered as information retrieval systems. This opens the hypertext field to various other types of systems that traditionally would not be considered as part of the field. The change of view enables a deeper fusion of human and machine. In particular, today's AI-based, intelligent systems open the demand of synthesising automation (on the machine's side) and augmentation (on the user's side). This article is not about researching AI systems; it is about extending the view of hypertext systems to synthesise augmentation and automation. We specifically apply this view to intelligent systems, asking the question about how hypertext can act as a common medium between human and machine, particularly for knowledge intensive tasks. We propose spatial hypertext as a medium that enables users to create cognitive maps. Along these lines, we provide examples from multiple projects and examine how these applications can be productive. [ABSTRACT FROM AUTHOR]