Malleable Overview-Detail Interfaces

2025-03-10

AI Summary

Existing overview-detail interfaces are predominantly static and fixed, limiting adaptability to users’ heterogeneous information needs—e.g., prioritizing price while suppressing location or rating. This paper introduces the *plastic overview-detail interface*, the first systematic formulation of its three-dimensional plasticity: *content* (dynamic attribute selection and generation), *composition* (adaptive multi-view orchestration), and *layout* (semantics-aware automatic transformation). Methodologically, we integrate Fluid Attributes, AI-driven attribute reconstruction, a multi-view composition engine, and a layout transformation algorithm. A user study demonstrates that participants efficiently construct diverse, task-specific interface variants within minutes, significantly improving task completion rates and subjective satisfaction. Our work establishes a scalable theoretical framework and practical methodology for personalizing interactive interfaces, advancing the design of adaptive, user-centered visualization systems.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The overview-detail design pattern, characterized by an overview of multiple items and a detailed view of a selected item, is ubiquitously implemented across software interfaces. Designers often try to account for all users, but ultimately these interfaces settle on a single form. For instance, an overview map may display hotel prices but omit other user-desired attributes. This research instead explores the malleable overview-detail interface, one that end-users can customize to address individual needs. Our content analysis of overview-detail interfaces uncovered three dimensions of variation: content, composition, and layout, enabling us to develop customization techniques along these dimensions. For content, we developed Fluid Attributes, a set of techniques enabling users to show and hide attributes between views and leverage AI to manipulate, reformat, and generate new attributes. For composition and layout, we provided solutions to compose multiple overviews and detail views and transform between various overview and overview-detail layouts. A user study on our techniques implemented in two design probes revealed that participants produced diverse customizations and unique usage patterns, highlighting the need and broad applicability for malleable overview-detail interfaces.

Problem

• Explores customizable overview-detail interfaces for user needs.
• Develops techniques for content, composition, and layout customization.
• Demonstrates diverse user customizations and unique usage patterns.

Innovation

• Fluid Attributes for customizable content display
• AI-driven attribute manipulation and generation
• Dynamic composition and layout transformation techniques