Work

Time-Series Recommendation Quality, Algorithm Aversion, and Data-Driven Decisions: A Temporal Human–AI Interaction Perspective

Year: 2025

Type: article

Abstract: New AI technologies have empowered e-commerce personalized recommendation systems, many of which now leverage time-series forecasting to capture dynamic user preferences. However, buyers’ algorithm aversion hinders these systems from realizing their full potential in enabling data-driven decisions. Current research focuses heavily on artifact design and algorithm optimization to reduce aversion, with insufficient attention to the temporal dimensions of human–AI interaction (HAII). To address this gap, this study explores how recommendation accuracy, novelty, and diversity—key attributes in time-series recommendation contexts—influence buyers’ algorithm aversion from a temporal HAII perspective. Data from 205 online survey responses were analyzed using partial least squares structural equation modeling (PLS-SEM). Results reveal that accuracy (encompassing sequential prediction consistency), novelty (balanced with temporal relevance), and diversity (covering long-term preferences) negatively impact algorithm aversion, with perceived usefulness as a mediator. Reduced aversion further facilitates data-driven purchasing decisions. This study enriches the algorithm aversion literature by emphasizing temporal HAII in time-series recommendation scenarios, bridging human factors research with data-driven decision-making in e-commerce.

Source: Mathematics

DOI: 10.3390/math13213528

Authors: Shan Jiang, Tianyu Chen, Yufei Tan, Shiqi Gao, Lanhao Li

Institutions: Wuhan University of Technology, Beihang University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: Natural Science Foundation of Hubei Province, State Grid Corporation of China