Consumer Behavior

Classification of AI Recommendation Systems in Fashion Retail: From the Perspective of Consumer Decision-Making

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Abstract

This study proposes a consumer-centric classification of AI recommendation systems in fashion retail by mapping system functionalities to the five stages of the consumer decision-making process. Existing classifications emphasize algorithmic or technical features, yet overlook how AI shapes need activation, search behavior, evaluation, purchase decisions, and post-purchase engagement. Using a systematic thematic analysis of academic literature, industry reports, and commercial documentation, the study identifies six categories of AI systems, ranging from trend-driven discovery tools to post-purchase support and multi-stage decision assistants. The resulting framework clarifies how AI influences consumer perceptions and choices at distinct decision points, offering theoretical insight into the behavioral mechanisms of AI-mediated shopping. Practically, this classification guides retailers in strategically deploying AI tools to enhance personalization, streamline decisions, and improve consumer experience across the shopping journey.

Keywords: AI recommendation systems, fashion retail, consumer decision-making, classification

How to Cite:

Cai, Y., Lee, H. & Xu, Y., (2025) “Classification of AI Recommendation Systems in Fashion Retail: From the Perspective of Consumer Decision-Making”, International Textile and Apparel Association Annual Conference Proceedings 82(1). doi: https://doi.org/10.31274/itaa.21801