Hesitation and Tolerance in Recommender Systems

TL;DR

This paper identifies hesitation and tolerance as key user states in recommender systems, revealing their impact on user experience and proposing strategies to improve engagement and reduce wasted effort.

Contribution

It introduces the concepts of hesitation and tolerance in user interactions, supported by large-scale surveys and an online field study, to enhance recommender system design.

Findings

Hesitation is nearly universal among users.

Tolerance behaviors lead to wasted time and frustration.

Strategies treating tolerance separately improve retention.

Abstract

Users' interactions with recommender systems often involve more than simple acceptance or rejection. We highlight two overlooked states: hesitation, when people deliberate without certainty, and tolerance, when this hesitation escalates into unwanted engagement before ending in disinterest. Across two large-scale surveys (N=6,644 and N=3,864), hesitation was nearly universal, and tolerance emerged as a recurring source of wasted time, frustration, and diminished trust. Analyses of e-commerce and short-video platforms confirm that tolerance behaviors, such as clicking without purchase or shallow viewing, correlate with decreased activity. Finally, an online field study at scale shows that even lightweight strategies treating tolerance as distinct from interest can improve retention while reducing wasted effort. By surfacing hesitation and tolerance as consequential states, this work reframes how recommender systems should interpret feedback, moving beyond clicks and dwell time toward designs that respect user value, reduce hidden costs, and sustain engagement.