Critiquing-based Modeling of Subjective Preferences

TL;DR

This paper introduces collective criticism, a novel method that models subjective preferences from qualitative critiques by transforming them into censored intervals and analyzing with interval regression, applicable to diverse domains.

Contribution

It presents a new critiquing-based approach for modeling subjective preferences, enabling optimization from informal user critiques in continuous parameter spaces.

Findings

Successfully modeled aesthetic preferences for neural style transfer images.

Effectively captured user challenge experiences in Tetris.

Produced robust, interpretable models aligned with user feedback.

Abstract

Applications designed for entertainment and other non-instrumental purposes are challenging to optimize because the relationships between system parameters and user experience can be unclear. Ideally, we would crowdsource these design questions, but existing approaches are geared towards evaluation or ranking discrete choices and not for optimizing over continuous parameter spaces. In addition, users are accustomed to informally expressing opinions about experiences as critiques (e.g. it's too cold, too spicy, too big), rather than giving precise feedback as an optimization algorithm would require. Unfortunately, it can be difficult to analyze qualitative feedback, especially in the context of quantitative modeling. In this article, we present collective criticism, a critiquing-based approach for modeling relationships between system parameters and subjective preferences. We transform critiques, such as "it was too easy/too challenging", into censored intervals and analyze them using interval regression. Collective criticism has several advantages over other approaches: "too much/too little"-style feedback is intuitive for users and allows us to build predictive models for the optimal parameterization of the variables being critiqued. We present two studies where we model: (i) aesthetic preferences for images generated with neural style transfer, and (ii) users' experiences of challenge in the video game Tetris. These studies demonstrate the flexibility of our approach, and show that it produces robust results that are straightforward to interpret and inline with users' stated preferences.