Visualization has matured into an established research field, producing widely adopted tools, design frameworks, and empirical foundations. As the field has grown, ideas from outside computer science have increasingly entered visualization discourse, questioning the fundamental values and assumptions on which visualization research stands. In this short position paper, we examine a set of values that we see underlying the seminal works of Jacques Bertin, John Tukey, Leland Wilkinson, Colin Ware, and Tamara Munzner. We articulate three prominent values in these texts — universality, objectivity, and efficiency — and examine how these values permeate visualization tools, curricula, and research practices. We situate these values within a broader set of critiques that call for more diverse priorities and viewpoints. By articulating these tensions, we call for our community to embrace a more pluralistic range of values to shape our future visualization tools and guidelines.

The latitude of acceptance, or the range of recommendations users find acceptable, plays a critical role in recommender system effectiveness, yet how these acceptance boundaries evolve through user interaction remains poorly understood. This study investigates preference dynamics in gaming recommender systems with experienced players. We developed Pesto, an interactive tool that simulates Pokémon battles and visualizes how users’ latitude of acceptance shifts as they engage with different recommendations. Through a controlled within-user study with experienced players (N=10), we examine how exposure to varied recommendations influences the evolution of users’ acceptance latitude. Our study provides preliminary evidence that this approach can increase item acceptance without sacrificing user satisfaction. We also find a persistent upper bound on maximally acceptable recommendations across all participants. This work further suggests several concrete design implications based on a novel interactive visualization.

Group accommodation booking forces travelers to coordinate externally through messaging apps and informal voting, missing opportunities for transparent preference alignment. We present PRISM, an interactive group recommender system that transforms opaque recommendation processes into transparent collaborative visual experiences. PRISM employs a two-phase interaction paradigm: individual preference elicitation through conversational AI, followed by collaborative decision-making via bivariate map preference visualization. A controlled user study with 6 pairs shows PRISM enhances transparency (+1.83 on 5-point scale), consensus building (+2.0), and reduces conformity pressure compared to traditional approaches and interfaces.

Music streaming platforms reduce rich listening experiences to algorithmic black boxes, overlooking personal narratives that make music meaningful. We present Blooming Beats, an explainable recommender system that transforms Spotify listening data into visual narratives using Data Humanism principles. The system embodies TRACE principles: Transparency through visual explanations, Context-awareness by integrating personal context, and Empathy by matching listening stories rather than user profiles. A user study with 8 participants exploring a decade of listening data shows that narrative-driven visualization suggests potential for enhancing transparency and engagement.

Current music recommendation systems often lack transparency, preventing users from understanding the recommendations or effectively steering the algorithm. We present HUMMUS, an interactive collaborative music sequential recommender system that applies Giorgia Lupi’s Data Humanism principles to combine algorithmic transparency with human-centered design. HUMMUS visualizes songs as flowers, where petals represent audio features, and connecting lines reveal recommendation relationships. Real-time voting mechanisms during natural pauses in social interaction enable collaborative decision-making between humans and recommendation algorithms. Our mixed-methods evaluation, involving 19 participants, demonstrates that humanistic design principles enhance transparency, user engagement, and collaborative decision-making while maintaining the quality of recommendations. This work contributes to the intersection of critical visualization and explainable AI by demonstrating how Data Humanism can guide human-centered recommendation systems.

Recommender systems (RecSys) have become ubiquitous in users’ daily digital interactions, significantly influencing decision-making processes. As these systems grow in algorithmic complexity, effective explanations for non-expert users become essential to fostering understanding and trust. While academic research explores diverse explanation methods, commercial applications predominantly employ textual explanations due to their implementation efficiency and user familiarity. However, the effectiveness of these textual explanations is often compromised by suboptimal presentation within RecSys user interfaces (UIs), leading to reduced user engagement and comprehension. This issue is particularly relevant given the recent emergence of large language models (LLMs) for generating RecSys explanations. We introduce f-RecX, a conceptual framework for characterizing and designing effective textual explanations in RecSys UIs. Based on a two-phase methodology combining qualitative user studies and quantitative evaluations, f-RecX maps four input dimensions (Explanation Style, Goals, Domain Dynamics, and Recommender Systems Technique) to an output dimension focused on visual representation. The framework aims to enhance the’consumability’ of textual explanations by making them easier to locate and comprehend, and more valuable for non-expert users. We demonstrate f-RecX’s applicability through a usage scenario and analysis of existing RecSys UIs, offering valuable insights for enhancing explainability and user experience. • f-RecX uniquely bridges algorithmic and human-centered design by integrating four input dimensions (Explanation Style, Goals, Domain Dynamics, and Recommender Techniques) with visual presentation parameters, addressing the gap between explanation content generation and effective UI implementation. • The framework introduces the concept of explanation ”consumability” - how easily users can locate, understand, and derive value from explanations - providing empirically validated visual design guidelines including optimal font sizes (24-32px), positioning (top-left), and explanation length ( 20 words). • Through a comprehensive two-phase methodology combining empathy workshops and quantitative surveys, f-RecX provides design guidance that demonstrates how visual characteristics significantly impact explanation effectiveness, particularly relevant for LLM-generated explanations in commercial applications.

Item rankings are useful when a decision needs to be made, especially if there are multiple attributes to be considered. However, existing tools do not support both categorical and numerical attributes, require programming expertise for expressing preferences on attributes, do not offer instant feedback, lack flexibility in expressing various types of user preferences, or do not support all mandatory steps in the ranking-creation workflow. In this work, we present RankASco: a human-centered visual analytics approach that supports the interactive and visual creation of rankings. The iterative design process resulted in different visual interfaces that enable users to formalize their preferences based on a taxonomy of attribute scoring functions. RankASco enables broad user groups to (a) select attributes of interest, (b) express preferences on attributes through interactively tailored scoring functions, and (c) analyze and refine item ranking results. We validate RankASco in a user study with 24 participants in comparison to a general purpose tool. We report on commonalities and differences with respect to usefulness and usability and ultimately present three personas that characterize common user behavior in ranking-creation. On the human factors side, we have also identified a series of interesting behavioral variables that have an influence on the task performance and may shape the design of human-centered ranking solutions in the future.