The Impact of Personalized Recommendation Algorithms on Consumers' Purchasing Decisions

Abstract

Against the backdrop of ubiquitous big data and algorithmic mediation, recommender systems have become the dominant channel through which consumers discover products and form preferences. As artificial intelligence advances and platform economies expand, algorithmic recommendations now permeate every stage of digital consumption, reconfiguring the interactions among users, goods, and marketplaces. On e-commerce platforms in particular, personalized recommender systems serve not only as engines of conversion but also as subtle architects of decision paths and preference structures. The present study combines survey evidence with click-stream data to examine the causal impact of these systems on purchase behavior. Results indicate that algorithmic recommendations significantly increase click-through and purchase intentions, yet tension persists between accuracy and diversity. Users exhibit a calculated trade-off—surrendering a degree of control in exchange for cognitive efficiency. Drawing on questionnaire responses and secondary click data from Taobao, the paper underscores the need for governance frameworks that balance commercial effectiveness with user autonomy.