One classic issue associated with being a researcher nowadays is the multitude and magnitude of search results for a given topic. Recommender systems can help to fix this problem by directing users to the resources most relevant to their specific research focus. However, sets of automatically generated recommendations are likely to contain irrelevant resources, making user interfaces that provide effective filtering mechanisms necessary. This problem is exacerbated when users resume a previously interrupted research task, or when different users attempt to tackle one extensive list of results, as confusion as to what resources should be consulted can be overwhelming. The presented recommendation dashboard uses micro-visualisations to display the state of multiple filters in a data type-specific manner. This paper describes the design and geometry of micro-visualisations and presents results from an evaluation of their readability and memorability in the context of exploring recommendation results. Based on that, this paper also proposes applying micro-visualisations for extending the use of a desktop-based dashboard to the needs of small-screen, mobile multi-touch devices, such as smartphones. A small-scale heuristic evaluation was conducted using a first prototype implementation.

如今，与成为研究者有关的一个经典问题是给定主题的搜索结果的数量和范围。推荐系统可以通过将用户引导到与他们的特定研究重点最相关的资源来帮助解决此问题。但是，自动生成的建议集可能包含不相关的资源，因此提供有效过滤机制的用户界面必不可少。当用户继续执行先前中断的研究任务时，或者当不同的用户尝试处理一个广泛的结果列表时，这个问题会更加严重，因为对于应咨询哪些资源的困惑可能会变得不知所措。呈现的推荐仪表板使用微观可视化以特定于数据类型的方式显示多个过滤器的状态。本文介绍了微可视化的设计和几何结构，并在探索推荐结果的背景下介绍了其可读性和记忆性的评估结果。基于此，本文还建议应用微观可视化，以将基于桌面的仪表板的使用扩展到小屏幕，移动多点触摸设备（如智能手机）的需求。使用第一个原型实现进行了小规模的启发式评估。