Interactive Visualization Recommendation with Hier-SUCB

TL;DR

This paper introduces Hier-SUCB, an interactive recommendation system for visualizations that learns from user feedback, improving personalization and adaptability over previous non-interactive models.

Contribution

The paper presents Hier-SUCB, a novel contextual combinatorial semi-bandit algorithm for interactive visualization recommendation, with theoretical regret bounds and superior experimental performance.

Findings

Hier-SUCB achieves lower regret bounds compared to existing algorithms.

It outperforms other bandit algorithms in visualization recommendation tasks.

The system effectively adapts to user feedback for personalized recommendations.

Abstract

Visualization recommendation aims to enable rapid visual analysis of massive datasets. In real-world scenarios, it is essential to quickly gather and comprehend user preferences to cover users from diverse backgrounds, including varying skill levels and analytical tasks. Previous approaches to personalized visualization recommendations are non-interactive and rely on initial user data for new users. As a result, these models cannot effectively explore options or adapt to real-time feedback. To address this limitation, we propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions. For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual combinatorial semi-bandit in the PVisRec setting. Theoretically, we show an improved overall regret bound with the same rank of time but an improved rank of action space. We further demonstrate the effectiveness of Hier-SUCB through extensive experiments where it is comparable to offline methods and outperforms other bandit algorithms in the setting of visualization recommendation.