Trustworthy Preference Completion in Social Choice

TL;DR

This paper introduces a trust-based preference completion method that estimates individual preferences from noisy partial rankings using a trust-oriented neighbor search and statistical measures, validated through experiments.

Contribution

It proposes a novel trust-based anchor-kNN algorithm and a statistical measurement for certainty and conflict in preference completion, addressing noise and irrational behaviors.

Findings

The approach effectively handles noisy and irrational rankings.

Experimental results outperform state-of-the-art methods.

Properties of certainty and conflict are empirically validated.

Abstract

As from time to time it is impractical to ask agents to provide linear orders over all alternatives, for these partial rankings it is necessary to conduct preference completion. Specifically, the personalized preference of each agent over all the alternatives can be estimated with partial rankings from neighboring agents over subsets of alternatives. However, since the agents' rankings are nondeterministic, where they may provide rankings with noise, it is necessary and important to conduct the trustworthy preference completion. Hence, in this paper firstly, a trust-based anchor-kNN algorithm is proposed to find $k$-nearest trustworthy neighbors of the agent with trust-oriented Kendall-Tau distances, which will handle the cases when an agent exhibits irrational behaviors or provides only noisy rankings. Then, for alternative pairs, a bijection can be built from the ranking space to the preference space, and its certainty and conflict can be evaluated based on a well-built statistical measurement Probability-Certainty Density Function. Therefore, a certain common voting rule for the first $k$ trustworthy neighboring agents based on certainty and conflict can be taken to conduct the trustworthy preference completion. The properties of the proposed certainty and conflict have been studied empirically, and the proposed approach has been experimentally validated compared to state-of-arts approaches with several data sets.