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Visualization recommendation systems make data exploration less tedious by automating the process of visualization generation. They are particularly helpful for non-expert users who may not be familiar with a data set or the process of visualization specification. These systems allow users to input their preferences in the form of partial specifications to steer the recommendations made. However, the interaction approaches for partial specification input and their trade-offs have not been explored in prior work. In this paper, we compare three different combinations of interaction approaches and granularities for users to indicate a preferred partial specification: 1) manual input, 2) inferring preferred partial specifications from binary like/dislike ratings for a visualization as a whole, or 3) inferring preferred partial specifications from binary like/dislike ratings for granular components of a visualization specification. In a between-subjects study, participants were assigned to one of three conditions and asked to complete a data exploration task. Our results indicate that manual input led to a greater coverage of data dimensions, while like/dislike ratings led to a greater diversity of marks and channels used. Qualitative participant feedback also reveals differences in user strategy and visualization comprehension across the three interaction conditions. Finally, we conclude with a discussion on implications for multiplicity and visualization comprehension during visual data exploration.

We present Libra+, a high-level conceptual model and library for interactive data visualization that supports the composition of primitive interactions into sophisticated ones. Libra+ formalizes interactions through a Trigger–Target–Feedback model, which specifies input events that initiate an interaction, the target on which it operates, and the resulting feedback. This model serves as a semantic bridge between high-level interaction intent and its modular, concrete implementation. To operationalize this approach, we define atomic instruments as reusable interaction units and introduce coordination strategies for composing them across graphical layers and views. By resolving input conflicts, supporting cross-layer synchronization, and enabling customizable feedback, Libra+ provides both a vocabulary for interaction design and a practical framework for building modular, extensible, and composable interactions for visualizations. We implement this framework as an extension of Libra.js and demonstrate that Libra+ can reproduce sophisticated interactive visualizations and generalize existing interactions to new contexts through a set of use cases. By enabling the parametrized composition of instruments, our unified interaction framework promotes modularity and extensibility in interaction design.

In large-scale text analysis tasks, pre-trained language models are often used to embed text corpora for downstream analysis. However, such models may struggle to capture domain-specific semantics and adapting them typically requires large amounts of labeled data and technical expertise to implement training pipelines. Recent approaches have demonstrated how visual interactions in document projections can capture human feedback as training signals for model tuning. However, these methods operate on document-level feedback, which requires users to open and assess individual documents in order to provide effective feedback. In this paper, we propose KeySI, an interaction framework that enables feature-level feedback through keyword-based concept specification. Users specify feedback by organizing extracted keywords into groups representing concepts, which KeySI translates into document-level supervision for subsequent tuning. By operating on keywords as the primary interaction medium, KeySI reduces the need for manual document inspection and labeling and lowers the barrier to adapting embedding models. We present a prototype implementation that, given a corpus, curates representative keywords, visualizes keywords and document embeddings via dimensionality reduction, allows interactive specification of keyword groups, and supports iterative refinement through system feedback. We evaluate KeySI through a user study, usage scenarios, and quantitative experiments demonstrating its effectiveness in capturing user intent and improving embedding alignment.

Unit visualization represents each data item as a visual element, facilitating intuitive understanding while preserving individual data identity. However, existing tools for creating unit visualizations are constrained by rigid, rule-based layouts and regular geometric shapes, limiting the flexibility and creativity in their visual expression. Informed by a study of 602 existing examples, we introduce UnitoPia, an interactive authoring system for pictorial unit visualization, enabling the creation of unit visualizations with irregular visual elements in expressive organic layouts. Built upon an intuitive contain-and-fill design model, UnitoPia allows users to construct pictorial unit visualizations through direct manipulation of four tangible concepts: marks, container, emitter, and force. To realize this design model, we introduce image-space-based layout optimization via differentiable rendering, which efficiently contains and fills arbitrary visual elements within containers of any shape. A wide range of examples demonstrates UnitoPia's versatility and expressiveness, while a user study confirms its usability and effectiveness. UnitoPia is available with its source code at https://unitopia-web.github.io/.

Visualization research has long examined embellishment and animation, while the design of rich interaction-contingent feedback remains under-articulated as a systematic design space. We introduce JuicyVIS, a theory-informed operationalization of juicy feedback for interactive visualization that translates ideas from game studies into a visualization-centered framework. JuicyVIS introduces three dimensions, interaction type, feedback timing, and feedback intensity, which we explore through 26 controlled prototypes, grounded in established visualization interaction categories and prior work on juicy feedback in games. We evaluate the juicy design space through three exploratory mixed-methods online studies. Results show that juicy feedback was consistently associated with higher engagement and aesthetic experience, while the clearest gains came from post-interaction feedback, not, for example, by simply increasing intensity. We discuss how juicy feedback can be thought of as a design material in visualizations, shaping how visualization interactions feel, how action consequences are perceived, and how users remain engaged with data representations.

Brushing is a common interaction technique in 2D scatterplots, allowing users to select clustered points within a continuous, enclosed region for further analysis or filtering. However, applying conventional brushing to 2D representations of multidimensional (MD) data, i.e., Multidimensional Projections (MDPs), can lead to unreliable cluster analysis due to MDP-induced distortions that inaccurately represent the cluster structure of the original MD data. To alleviate this problem, we introduce a novel brushing technique for MDPs called Distortion-aware brushing. As users perform brushing, Distortion-aware brushing correct distortions around the currently brushed points by dynamically relocating points in the projection, pulling data points close to the brushed points in MD space while pushing distant ones apart. This dynamic adjustment helps users brush MD clusters more accurately, leading to more reliable cluster analysis. Our user studies with 24 participants show that Distortion-aware brushing significantly outperforms previous brushing techniques for MDPs in accurately separating clusters in the MD space and remains robust against distortions. We further demonstrate the effectiveness of our technique through two use cases: (1) conducting cluster analysis of geospatial data and (2) interactively labeling MD clusters.

Modern data analysis requires speed for massive datasets. Progressive Data Analysis and Visualization (PDAV) emerged as a discipline to address this problem, providing fast response times while maintaining interactivity with controlled accuracy. Yet it remains difficult to implement and reproduce. To lower this barrier, we present ProVega, a Vega-Lite-based grammar that simplifies PDAV instrumentation for both simple visualizations and complex visual environments. Alongside it, we introduce Pro-Ex, an editor designed to streamline the creation and analysis of progressive solutions. We validated ProVega by reimplementing 11 exemplars from the literature—verified for fidelity by 39 users—and demonstrating its support for various progressive methods, including data-chunking, process-chunking, and mixed-chunking. An expert user study confirmed the efficacy of ProVega and the Pro-Ex environment in real-world tasks. ProVega, Pro-Ex, and all related materials are available at https://github.com/XAIber-lab/provega