Recommenders play a significant role in our daily lives, making decisions for users on a regular basis. Their widespread adoption necessitates a thorough examination of how users interact with recommenders and the algorithms that drive them. An important form of interaction in these systems are algorithmic affordances: means that provide users with perceptible control over the algorithm by, for instance, providing context (‘find a movie for this profile’), weighing criteria (‘most important is the main actor’), or evaluating results (‘loved this movie’). The assumption is that these algorithmic affordances impact interaction qualities such as transparency, trust, autonomy, and serendipity, and as a result, they impact the user experience. Currently, the precise nature of the relation between algorithmic affordances, their specific implementations in the interface, interaction qualities, and user experience remains unclear. Subjects that will be discussed during the workshop, therefore, include but are not limited to the impact of algorithmic affordances and their implementations on interaction qualities, balances between cognitive overload and transparency in recommender interfaces containing algorithmic affordances; and reasons why research into these types of interfaces sometimes fails to cross the research-practice gap and are not landing in the design practice. As a potential solution the workshop committee proposes a library of examples of algorithmic affordances design patterns and their implementations in recommender interfaces enriched with academic research concerning their impact. The final part of the workshop will be dedicated to formulating guiding principles for such a library.
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This exploratory study investigates the rationale behind categorizing algorithmic controls, or algorithmic affordances, in the graphical user interfaces (GUIs) of recommender systems. Seven professionals from industry and academia took part in an open card sorting activity to analyze 45 cards with examples of algorithmic affordances in recommender systems’ GUIs. Their objective was to identify potential design patterns including features on which to base these patterns. Analyzing the group discussions revealed distinct thought processes and defining factors for design patterns that were shared by academic and industry partners. While the discussions were promising, they also demonstrated a varying degree of alignment between industry and academia when it came to labelling the identified categories. Since this workshop is part of the preparation for creating a design pattern library of algorithmic affordances, and since the library aims to be useful for both industry and research partners, further research into design patterns of algorithmic affordances, particularly in terms of labelling and description, is required in order to establish categories that resonate with all relevant parties
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Introduction: Sensor-feedback systems can be used to support people after stroke during independent practice of gait. The main aim of the study was to describe the user-centred approach to (re)design the user interface of the sensor feedback system “Stappy” for people after stroke, and share the deliverables and key observations from this process. Methods: The user-centred approach was structured around four phases (the discovery, definition, development and delivery phase) which were fundamental to the design process. Fifteen participants with cognitive and/or physical limitations participated (10 women, 2/3 older than 65). Prototypes were evaluated in multiple test rounds, consisting of 2–7 individual test sessions. Results: Seven deliverables were created: a list of design requirements, a personae, a user flow, a low-, medium- and high-fidelity prototype and the character “Stappy”. The first six deliverables were necessary tools to design the user interface, whereas the character was a solution resulting from this design process. Key observations related to “readability and contrast of visual information”, “understanding and remembering information”, “physical limitations” were confirmed by and “empathy” was additionally derived from the design process. Conclusions: The study offers a structured methodology resulting in deliverables and key observations, which can be used to (re)design meaningful user interfaces for people after stroke. Additionally, the study provides a technique that may promote “empathy” through the creation of the character Stappy. The description may provide guidance for health care professionals, researchers or designers in future user interface design projects in which existing products are redesigned for people after stroke.
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