Multi-criteria decision making in large databases is very important in real world applications. Recently, an interactive query has been studied extensively in the database literature with the advantage of both the top-k query (with limited output size) and the skyline query (which does not require users to explicitly specify their preference function). This approach iteratively asks the user to select the one preferred within a set of options. Based on rounds of feedback, the query learns the implicit preference and returns the most favorable as a recommendation. However, many modern applications in areas like housing or financial product markets feature datasets with hundreds of attributes. Existing interactive algorithms either fail to scale or require excessive user interactions (often exceeding 1000 rounds). Motivated by this, we propose FHDR (Fast High-Dimensional Reduction), a novel framework that takes less than 0.01s with fewer than 30 rounds of interaction. It is considered a breakthrough in the field of interactive queries since most, if not all, existing studies are not scalable to high-dimensional datasets. Extensive experiments demonstrate that FHDR outperforms the best-known algorithms by at least an order of magnitude in execution time and up to several orders of magnitude in terms of the number of interactions required, establishing a new state of the art for scalable interactive regret minimization.

翻译：大规模数据库中的多准则决策在实际应用中至关重要。近年来，交互式查询在数据库领域得到了广泛研究，它兼具top-k查询（输出规模有限）和天际线查询（无需用户显式指定偏好函数）的优势。该方法通过迭代方式要求用户在一组选项中选择偏好项，基于多轮反馈学习隐式偏好，并返回最优推荐结果。然而，在住房或金融产品市场等现代应用领域中，数据集往往包含数百个属性维度。现有交互式算法要么无法扩展至高维场景，要么需要过多的用户交互轮次（通常超过1000轮）。针对这一挑战，我们提出了FHDR（快速高维约简）框架，该框架能在少于30轮交互、低于0.01秒的时间内完成计算。由于现有研究大多无法扩展至高维数据集，该成果被视为交互式查询领域的突破性进展。大量实验表明，FHDR在运行时间上比当前最优算法提升至少一个数量级，在所需交互轮次上提升数个数量级，为可扩展的交互式遗憾最小化建立了新的技术标杆。