在K-最近邻查询结果中提供多样性
Providing Diversity in K-Nearest Neighbor Query Results
摘要：
给定多维空间中的一个点查询Q，KNN查询根据数据库中关于Q的给定距离度量返回K个最近的答案。在这种情况下，大多数答案可能与其他答案非常相似，特别是当数据具有聚类时。对于各种应用程序，这种同质结果集可能不会为用户增加价值。在本文中，我们考虑的问题，提供多样性的KNN查询的结果，也就是说，产生最接近的结果集，使每个答案是充分不同的休息。我们首先提出了一个用户可调的多样性定义，然后提出了一个算法，称为MOTLEY，根据这个定义产生一个不同的结果集。通过对真实数据和合成数据的详细实验评估，我们表明，MOTLEY可以产生不同的结果集，只需读取数据库中的元组的一小部分。此外，它不对传统KNN查询的评估施加额外的开销，从而提供多样性和距离之间的无缝接口。
英文摘要：
Given a point query Q in multi-dimensional space, K-Nearest Neighbor (KNN) queries return the K closest answers according to given distance metric in the database with respect to Q. In this scenario, it is possible that a majority of the answers may be very similar to some other, especially when the data has clusters. For a variety of applications, such homogeneous result sets may not add value to the user. In this paper, we consider the problem of providing diversity in the results of KNN queries, that is, to produce the closest result set such that each answer is sufficiently different from the rest. We first propose a user-tunable definition of diversity, and then present an algorithm, called MOTLEY, for producing a diverse result set as per this definition. Through a detailed experimental evaluation on real and synthetic data, we show that MOTLEY can produce diverse result sets by reading only a small fraction of the tuples in the database. Further, it imposes no additional overhead on the evaluation of traditional KNN queries, thereby providing a seamless interface between diversity and distance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
