信息资源管理学报 ›› 2025, Vol. 15 ›› Issue (5): 116-130.doi: 10.13365/j.jirm.2025.05.116

• 研究论文 • 上一篇    下一篇

个性化推荐情境下算法素养知识启发措施与提升模式识别研究

刘静1　白芳睿2　吴丹 2,3   

  1. 1.四川大学公共管理学院，成都，610065；　
    2.武汉大学信息管理学院，武汉，430072；　
    3.武汉大学人机交互与用户行为研究中心，武汉，430072
  • 出版日期:2025-09-26 发布日期:2025-10-31
  • 作者简介:刘静，博士后、助理研究员，研究方向为算法素养、信息行为；白芳睿，博士研究生，研究方向为信息行为；吴丹（通讯作者），教授，博士生导师，研究方向为人机交互，Email：woodan@whu.edu.cn。
  • 基金资助:
    本文系国家自然科学基金“可解释、可通用的下一代人工智能方法”重大研究计划培育项目“人机交互视角下数据与知识双驱动的可解释智能决策方法研究”（92370112）及湖北省自然科学基金创新群体项目“以人为本的人工智能创新应用”（2023AFA012）的阶段性成果之一。

Identifying Knowledge Heuristic Intervention and Enhancement Patterns for Algorithmic Literacy in Personalized Recommendation Contexts

Liu Jing1　Bai Fangrui2　Wu Dan2,3   

  1. 1. School of Public Administration, Sichuan University, Chengdu, 610065;　
    2. School of Information Management, Wuhan University, Wuhan,430072;　
    3. Center for Studies of Human-Computer Interaction and User Behavior, Wuhan University, Wuhan, 430072
  • Online:2025-09-26 Published:2025-10-31
  • About author:Liu Jing, Ph.D, postdoctoral, assistant researcher, research interests include algorithmic literacy and information behavior; Bai Fangrui, Ph.D, candidate, research interests include user information behavior; Wu Dan(corresponding author), Ph.D, professor, Ph.D supervisor, research interests include human-computer interaction research, Email: woodan@whu.edu.cn.
  • Supported by:
    This paper is one of the outcomes of the Major Research Program "Explainable and Universal Next-Generation Artificial Intelligence Methods"(92370112) supported by National Natural Science Foundation of China, and the Innovation Group Project "Human-Centered Innovative Applications of Artificial Intelligence"(2023AFA012) supported by Hubei Natural Science Foundation.

PDF

15

摘要： 个性化推荐是算法对人们日常生活影响最为深刻的应用情境之一，也是信息控制权向算法转移的重要标志。这种信息世界的权利偏移对人们感知、理解和应用个性化推荐算法提出了新的要求，面向个性化推荐这一具体情境的算法素养研究亟待开展。本研究聚焦个性化推荐情境，设计了算法素养提升的知识启发措施，并面向30名被试开展了为期4周的纵向追踪实验，对算法素养变化情况进行统计分析。结果显示，实验前后用户算法素养各维度能力均有提升，验证了知识启发措施的有效性；通过聚类分析，识别出算法素养的三种提升模式：弱基础渐进提升模式、弱动机知识技能提升模式和强动机感知提升模式。本研究进一步分析了不同模式的用户特征，并针对不同模式的特点提出算法素养的知识启发策略。

关键词: 算法素养, 个性化推荐, 知识启发, 启发措施, 提升模式

Abstract: Personalized recommendations significantly influence daily life and represent a key shift in information control toward algorithms. This change necessitates new skills for individuals to perceive, understand, and utilize these algorithms effectively, highlighting an urgent need for research on algorithmic literacy within the context of personalized recommendations. This study concentrated on personalized recommendation contexts, developing a knowledge heuristic intervention to enhance algorithmic literacy. A 4-week longitudinal user experiment involving 30 participants was conducted, with statistical comparisons and analyses of changes in algorithmic literacy, as well as the identification of enhancement patterns through cluster analysis. Before and after the experiment, users showed significant improvements in different dimensions of algorithmic literacy, confirming the effectiveness of the knowledge heuristic intervention. Cluster analysis identified three enhancement patterns of algorithmic literacy: gradual improvement with weak foundations pattern, Knowledge-Skill enhancement with weak motivation pattern, and awareness enhancement with strong motivation pattern. This study further analyzed user characteristics associated with each pattern and proposed tailored knowledge heuristic strategies for enhancing algorithmic literacy based on these characteristics.

Key words: Algorithmic literacy, Personalized recommendation, Knowledge heuristic, Heuristic intervention, Enhancement patterns

中图分类号: 

地址：武汉市武昌路珞珈山 武汉大学信息管理学院 邮编：430072 电话：027-68754779 E-mail：xxzyglxb@163.com
版权所有 © 《信息资源管理学报》编辑部     鄂ICP备05003330号-1
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持：support@magtech.com.cn