High-dimensional Regret Minimization

TL;DR

This paper introduces FHDR, a highly scalable interactive algorithm for high-dimensional regret minimization in large datasets, significantly reducing user interactions and execution time compared to previous methods.

Contribution

The paper presents FHDR, a novel framework that efficiently handles high-dimensional datasets in interactive decision making, overcoming scalability issues of existing algorithms.

Findings

FHDR reduces execution time by at least an order of magnitude.

FHDR requires fewer than 30 interaction rounds.

FHDR outperforms existing algorithms in high-dimensional settings.

Abstract

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.