Efficient Algorithms for k-Regret Minimizing Sets

TL;DR

This paper proves the NP-Completeness of k-regret minimization for dimensions three and higher, and introduces two efficient approximation algorithms with guarantees, outperforming previous methods in speed and scalability.

Contribution

It establishes the complexity of k-regret minimization for all dimensions and proposes two new approximation algorithms with provable guarantees.

Findings

NP-Completeness for d >= 3 proven

Two approximation schemes introduced with guarantees

Algorithms are faster and more scalable than previous methods

Abstract

A regret minimizing set Q is a small size representation of a much larger database P so that user queries executed on Q return answers whose scores are not much worse than those on the full dataset. In particular, a k-regret minimizing set has the property that the regret ratio between the score of the top-1 item in Q and the score of the top-k item in P is minimized, where the score of an item is the inner product of the item's attributes with a user's weight (preference) vector. The problem is challenging because we want to find a single representative set Q whose regret ratio is small with respect to all possible user weight vectors. We show that k-regret minimization is NP-Complete for all dimensions d >= 3. This settles an open problem from Chester et al. [VLDB 2014], and resolves the complexity status of the problem for all d: the problem is known to have polynomial-time solution for d <= 2. In addition, we propose two new approximation schemes for regret minimization, both with provable guarantees, one based on coresets and another based on hitting sets. We also carry out extensive experimental evaluation, and show that our schemes compute regret-minimizing sets comparable in size to the greedy algorithm proposed in [VLDB 14] but our schemes are significantly faster and scalable to large data sets.