Preference Elicitation in Prioritized Skyline Queries

TL;DR

This paper introduces p-skyline queries that allow varying attribute importance in preference relations, providing an efficient method for eliciting user preferences based on example sets, with proven computational complexity results.

Contribution

It generalizes skyline queries by incorporating attribute importance, studies their properties, and develops an elicitation algorithm with complexity analysis and experimental validation.

Findings

Elicitation without inferior examples is polynomial-time solvable.

Elicitation with inferior examples is NP-complete.

The proposed algorithm achieves high accuracy and scalability.

Abstract

Preference queries incorporate the notion of binary preference relation into relational database querying. Instead of returning all the answers, such queries return only the best answers, according to a given preference relation. Preference queries are a fast growing area of database research. Skyline queries constitute one of the most thoroughly studied classes of preference queries. A well known limitation of skyline queries is that skyline preference relations assign the same importance to all attributes. In this work, we study p-skyline queries that generalize skyline queries by allowing varying attribute importance in preference relations. We perform an in-depth study of the properties of p-skyline preference relations. In particular,we study the problems of containment and minimal extension. We apply the obtained results to the central problem of the paper: eliciting relative importance of attributes. Relative importance is implicit in the constructed p-skyline preference relation. The elicitation is based on user-selected sets of superior (positive) and inferior (negative) examples. We show that the computational complexity of elicitation depends on whether inferior examples are involved. If they are not, elicitation can be achieved in polynomial time. Otherwise, it is NP-complete. Our experiments show that the proposed elicitation algorithm has high accuracy and good scalability