View recommendations have emerged as a powerful tool to assist data analysts in exploring and understanding big data. Existing view recommendation approaches proposed a variety of utility functions in selecting useful views. However, the suitability of the utility functions and their tunable parameters for an analysis is usually dependent on the analysis context, such as the user, the data and the analysis task. In order to provide context-aware view recommendation, we formulate a new Interactive View Recommendation (IVR) paradigm, where the system interacts with the user to discover the utility functions that are most suitable in the current analysis context. We further develop an IVR framework, coined ViewSeeker, which leverages user feedback on intelligently selected example views to discover the most suitable utility functions. Finally, we implemented a prototype of ViewSeeker and verified its efficiency and effectiveness using two real-world datasets.

视图推荐已成为帮助数据分析师探索和理解大数据的强大工具。现有的视图推荐方法在选择有用的视图时提出了多种效用函数。然而，效用函数及其可调参数对分析的适用性通常取决于分析上下文，例如用户、数据和分析任务。为了提供上下文感知的视图推荐，我们制定了一个新的交互式视图推荐(IVR) 范式，其中系统与用户交互以发现最适合当前分析上下文的效用函数。我们进一步开发了一个 IVR 框架，创造了ViewSeeker，它利用用户对智能选择的示例视图的反馈来发现最合适的实用功能。最后，我们实现了ViewSeeker的原型，并使用两个真实世界的数据集验证了其效率和有效性。