Exploring the Role of Interaction Data to Empower End-User Decision-Making In UI Personalization

2026-03-19

AI Summary

This study addresses the challenge that users often lack effective support in recognizing and evaluating personalization opportunities within self-directed interface customization, leading to underutilization of available features. To bridge this gap, the paper proposes a “reflexive personalization” approach that guides users to reflect on their own interaction data, thereby enhancing their awareness of personalization value, facilitating trade-off assessments between benefits and effort, and improving the transparency of system-generated suggestions. Through an exploratory design probe employing experimental scenario scripts, semi-structured interviews, and qualitative analysis with twelve participants, the research demonstrates that while users can independently identify personalization opportunities, they strongly prefer system-provided visual recommendations. Interaction data significantly strengthens users’ willingness to change, heightens their perception of data value, and refines their personalization decision-making process.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

User interface personalization enhances digital efficiency, usability, and accessibility. However, in user-driven setups, limited support for identifying and evaluating worthwhile opportunities often leads to underuse. We explore a reflexive personalization approach where individuals engage with their digital interaction data to identify meaningful personalization opportunities and benefits. We interviewed 12 participants, using experimental vignettes as design probes to support reflection on different forms of using interaction data to empower decision-making in personalization and the preferred level of system support. We found that people can independently identify personalization opportunities but prefer system support through visual personalization suggestions. Interaction data can shape how users perceive and approach personalization by reinforcing the perceived value of change and data collection, helping them weigh benefits against effort, and increasing the transparency of system suggestions. We discuss opportunities for designing personalization software that raises end-users' agency over interfaces through reflective engagement with their interaction data.

Problem

• UI personalization
• interaction data
• end-user decision-making
• user agency
• personalization support

Innovation

• reflexive personalization
• interaction data
• user agency
• visual suggestions
• UI personalization