个性化推荐算法对用户决策行为影响研究综述

doi:10.11896/jsjkx.241100086

Abstract

Abstract: The rapid development of the Internet has generated massive data,and the phenomenon of information overload has thus become increasingly prominent.In order to help users effectively filter and capture data and make high-quality use of the huge amount of data,personalized recommendation algorithms have been proposed,and have been continuously developed in different scenarios and applications,and have a guiding role in the perception and decision-making behaviors of users.This paper focuses on four typical personalized recommendation algorithms,namely,collaborative filtering-based recommendation,content-based recommendation,association rule-based recommendation,and hybrid recommendation,analyzes their characteristics and applicability in the big data environment and different scenarios,and explores the development of personalized recommendation algorithms in the introduction of relevant theories and integration of emerging technologies from the perspective of Internet content platforms,e-commerce platforms,and social scenarios.The study also explores the influence of personalized recommendation algorithms on users’ decision-making behavior from the perspective of willingness to use and purchase decision,and then explores the functional role of personalized recommendation algorithms in users’ decision-making and related research prospects.

Key words: Personalized recommendations, Algorithm optimization, User decision-making behavior, E-commerce, Collaborative filtering, Machine learning

CLC Number:

TP391

XU Fuping, ZHOU Xiaohang, ZHANG Ning. Review of Impact of Personalized Recommendation Algorithms on User Decision-makingBehavior[J].Computer Science, 2025, 52(11A): 241100086-8.

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