Preference-based Search using Example-Critiquing with Suggestions

TL;DR

This paper enhances example-critiquing search tools by adding suggestion features based on user preferences, significantly increasing user engagement and the likelihood of finding the most preferred item.

Contribution

It introduces novel suggestion techniques for example-critiquing that analyze user preferences to better guide search, a new approach in preference-based interactive search.

Findings

Suggestions increase user preference expression by up to 78%.

Proposed methods are effective with both synthetic and real users.

Suggestions are highly attractive and improve search outcomes.

Abstract

We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%.