ABSTRACT

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.

摘要

推荐系统在我们的数字生活中无处不在。最值得注意的是，各种媒体平台指导我们选择视频，但推荐系统也用于更严肃的目标，例如新闻选择、政治倾向和工作决策。正如本次调查和立场文章中所述，基于推荐的提要范式已将用户行为从主动决策转变为被动地遵循推荐并接受被认为“足够好”的可能次优选择。我们提供媒体选择的历史概述，讨论推荐系统的假设和目标，并根据现有文献找出其缺点。然后，视角转变为超文本作为结构化信息和主动决策的范例。为了说明主动决策的相关性和重要性，我们提出了电视或媒体选择领域的一个用例，并（作为概念证明）转移到另一个应用领域：工业维护。在讨论部分，我们重点将这些操作分类为“系统 1”（快速和自动化）任务和“系统 2”（批判性思维）任务。此外，我们还讨论了用户如何从将主动（空间）结构化和分类与自动推荐相结合的工具中受益，以完成专业任务以及私人休闲活动。我们专注于将这些行动分类为“系统 1”（快速和自动化）任务和“系统 2”（批判性思维）任务。此外，我们还讨论了用户如何从将主动（空间）结构化和分类与自动推荐相结合的工具中受益，以完成专业任务以及私人休闲活动。我们专注于将这些行动分类为“系统 1”（快速和自动化）任务和“系统 2”（批判性思维）任务。此外，我们还讨论了用户如何从将主动（空间）结构化和分类与自动推荐相结合的工具中受益，以完成专业任务以及私人休闲活动。