基于用户实时反馈，动态推荐可视化方案。
Interactive Visualization Recommendation with Hier-SUCB
- 通过上下文组合半赌徒算法实现交互式推荐
- 在真实反馈下性能接近离线方法，优于其他带宽算法
- 适合需要快速适应不同用户需求的分析场景
可视化推荐旨在实现对大规模数据集的快速视觉分析。在实际应用中，快速获取并理解用户偏好至关重要，以满足不同背景、技能水平和分析任务的用户需求。以往个性化可视化推荐方法为非交互式，依赖新用户初始数据，无法有效探索选项或适应实时反馈。为此，我们提出一种交互式个性化可视化推荐（PVisRec）系统，基于历史交互中的用户反馈进行学习。为实现更高效准确的推荐，提出层级式SUCB（Hier-SUCB），一种适用于PVisRec场景的上下文组合半赌徒算法。理论上，我们证明了在时间复杂度相同的情况下，总体遗憾界得到改进，且动作空间的阶数更优。通过大量实验验证，Hier-SUCB性能可媲美离线方法，并在可视化推荐场景中超越其他带宽算法。
原文摘要 · Abstract (English)
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.
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