Misalignment, Learning, and Ranking: Harnessing Users Limited Attention

arXiv:2402.14013v1 Announce Type: new
Abstract: In digital health and EdTech, recommendation systems face a significant challenge: users often choose impulsively, in ways that conflict with the platform's long-term payoffs. This misalignment makes it difficult to effectively learn to rank items, as it may hinder exploration of items with greater long-term payoffs. Our paper tackles this issue by utilizing users' limited attention spans. We propose a model where a platform presents items with unknown payoffs to the platform in a ranked …

abstract arxiv attention challenge conflict cs.ds cs.lg digital digital health edtech exploration face health hinder learn long-term platform ranking recommendation recommendation systems systems type