Diverse Yet Efficient Retrieval using Hash Functions

TL;DR

This paper introduces a randomized locality sensitive hashing method that achieves high precision, diversity, and speed in retrieval tasks, including multi-label prediction, outperforming existing approaches by 100 times.

Contribution

The work presents a novel hashing-based retrieval method that balances accuracy, diversity, and efficiency simultaneously, extending to multi-label prediction and introducing new evaluation metrics.

Findings

Achieves 100x speed-up over existing methods.

Provides a good trade-off between accuracy and diversity.

Effective in diverse retrieval tasks including images and labels.

Abstract

Typical retrieval systems have three requirements: a) Accurate retrieval i.e., the method should have high precision, b) Diverse retrieval, i.e., the obtained set of points should be diverse, c) Retrieval time should be small. However, most of the existing methods address only one or two of the above mentioned requirements. In this work, we present a method based on randomized locality sensitive hashing which tries to address all of the above requirements simultaneously. While earlier hashing approaches considered approximate retrieval to be acceptable only for the sake of efficiency, we argue that one can further exploit approximate retrieval to provide impressive trade-offs between accuracy and diversity. We extend our method to the problem of multi-label prediction, where the goal is to output a diverse and accurate set of labels for a given document in real-time. Moreover, we introduce a new notion to simultaneously evaluate a method's performance for both the precision and diversity measures. Finally, we present empirical results on several different retrieval tasks and show that our method retrieves diverse and accurate images/labels while ensuring $100x$-speed-up over the existing diverse retrieval approaches.