Providing Diversity in K-Nearest Neighbor Query Results

Title: Providing Diversity in K-Nearest Neighbor Query Results

Abstract: This research aims to enhance the results of K-Nearest Neighbor (KNN) queries by introducing diversity. The authors propose a user-tunable definition of diversity and an algorithm called MOTLEY to produce diverse result sets. Through experimental evaluations on real and synthetic data, they demonstrate that MOTLEY can generate diverse results while imposing no additional overhead on traditional KNN queries.

Main Research Question: How can we enhance the results of K-Nearest Neighbor queries by introducing diversity?

Methodology: The authors first define diversity as a user-tunable parameter, MinDiv, which ranges from 0 to 1 and specifies the minimum diversity that should exist between any pair of answers in the result set. They then present the MOTLEY algorithm, which works by iteratively expanding the result set while maintaining diversity.

Results: The experimental results show that MOTLEY can produce diverse result sets by reading only a small fraction of the database tuples. Furthermore, it imposes no additional overhead on the evaluation of traditional KNN queries, providing a seamless interface between diversity and distance.

Implications: The MOTLEY algorithm offers a novel approach to enhancing the results of KNN queries by introducing diversity. This can be particularly beneficial in scenarios where users would prefer a more heterogeneous set of answers, such as when dealing with clustered data or when looking for a variety of options.

Limitations: While the MOTLEY algorithm significantly improves the diversity of KNN results, it may not always be able to find the most diverse set of answers, especially when the MinDiv parameter is set too high or when the data is highly clustered.

Future Work: Future research could focus on developing more sophisticated diversity measures and algorithms that can handle even more complex query scenarios. Additionally, further experimental evaluations could be conducted to better understand the performance and effectiveness of the MOTLEY algorithm in different real-world scenarios.

Link to Article: https://arxiv.org/abs/0310028v1 Authors: arXiv ID: 0310028v1