As an important tool for multi-criteria decision making in database systems, the regret minimization query is shown to have the merits of top-k and skyline queries: it controls the output size while does not need users to provide any preferences. Existing researches verify that the regret ratio can be much decreased when interaction is available. In this paper, we study how to enhance current interactive regret minimization query by sorting mechanism. Instead of selecting the most favorite point from the displayed points for each interaction round, users sort the displayed data points and send the results to the system. By introducing sorting mechanism, for each round of interaction the utility space explored will be shrunk to some extent. Further the candidate points selection for following rounds of interaction will be narrowed to smaller data spaces thus the number of interaction rounds will be reduced. We propose two effective sorting-based algorithms namely Sorting-Simplex and Sorting-Random to find the maximum utility point based on Simplex method and randomly selection strategy respectively. Experiments on synthetic and real datasets verify our Sorting-Simplex and Sorting-Random algorithms outperform current state-of-art ones.

中文翻译：

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基于排序的交互式遗憾最小化

作为数据库系统中多标准决策的重要工具，遗憾最小化查询具有top-k和skyline查询的优点：它控制输出大小，而不需要用户提供任何偏好。现有研究证实，当交互可用时，后悔率可以大大降低。在本文中，我们研究了如何通过排序机制来增强当前交互式后悔最小化查询。用户不是从每个交互回合的显示点中选择最喜欢的点，而是对显示的数据点进行排序并将结果发送到系统。通过引入排序机制，每轮交互探索的效用空间都会有所缩小。此外，后续轮次交互的候选点选择将缩小到更小的数据空间，从而减少交互轮次的数量。我们提出了两种有效的基于排序的算法，即 Sorting-Simplex 和 Sorting-Random，分别基于 Simplex 方法和随机选择策略来寻找最大效用点。在合成和真实数据集上的实验验证了我们的 Sorting-Simplex 和 Sorting-Random 算法优于当前最先进的算法。