Consumer decision-making mechanisms reconstruction by means of recommendation algorithms

Abstract

This article examines the reconstructive effect of recommendation algorithms on consumer decision-making mechanisms. Based on empirical research from the Netflix platform, it analyzes how recommendation systems reshape user decision pathways through cognitive mechanism manipulation. The research reveals that recommendation algorithms influence user decisions through the dual-system theoretical framework (System 1 and System 2), facilitating a transition from active search to passive acceptance. Through the consideration of global market data and user behavior patterns, the study validates the commercial effectiveness and potential challenges of algorithmic intervention, providing theoretical and practical guidance for the future development of recommendation systems