图书情报工作

图书情报工作 >

2025 , Vol. 69 >Issue 16: 66 - 78

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.16.006

研究论文

数字阅读平台个性化算法推荐服务功能需求类型及层次研究

  • 王璞钰 ,
  • 强薇 ,
  • 茆意宏
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  • 南京农业大学信息管理学院 南京 210095
王璞钰，博士研究生;强薇，硕士研究生;茆意宏，教授，博士，博士生导师，通信作者，E-mail:maoyh@njau.edu.cn。

收稿日期: 2024-07-29

  修回日期: 2024-11-15

  网络出版日期: 2025-08-01

基金资助

本文系国家社会科学基金重点项目“数智时代阅读服务转型研究”（项目编号： 21ATQ003）研究成果之一。

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Research on the Types and Levels of Functional Requirements for Personalized Algorithm Recommendation Services on Digital Reading Platforms

  • Wang Puyu ,
  • Qiang Wei ,
  • Mao Yihong
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  • College of Information Management, Nanjing Agricultural University, Nanjing 210095
Wang Puyu, doctoral candidate; Qiang Wei, master’s candidate; Mao Yihong, professor, PhD, doctoral supervisor, corresponding author, E-mail: maoyh@njau.edu.cn.

Received date: 2024-07-29

  Revised date: 2024-11-15

  Online published: 2025-08-01

Supported by

This work is supported by the key project of National Social Science Fund of China, titled “Research on the Transformation of Reading Services in the Digital Intelligence Era” (Grant No. 21ATQ003).

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摘要

[目的/意义] 揭示用户对数字阅读平台个性化算法推荐服务功能的需求类型及层次，为优化数字阅读平台个性化算法推荐服务提供参考。[方法/过程] 基于半结构化访谈、文献调研等方法，提炼数字阅读平台个性化算法推荐服务功能，结合Kano模型设计调查问卷，通过Kano数据分析、混合类分析、Better-Worse满意度指数分析与汇总，识别出数字阅读平台个性化算法推荐服务功能需求的层次，在此基础上提出优化策略。[结果/结论] 数字阅读平台个性化算法推荐服务功能可归纳为31项，其中推荐功能的解释与说明等3项功能属于必备型需求；推荐多种资源格式的读物的功能属于期望型需求；推荐界面布局等6项功能属于魅力型需求；实时推送、定时推送等20项功能属于无差异型需求；采集个人敏感信息的功能属于反向型需求。数字阅读平台应当根据个性化算法推荐服务功能的需求类型及层次优化相应服务。

关键词： 数字阅读; 个性化算法推荐; 用户需求; Kano模型

本文引用格式

王璞钰 , 强薇 , 茆意宏 . 数字阅读平台个性化算法推荐服务功能需求类型及层次研究[J]. 图书情报工作, 2025 , 69(16) : 66 -78 . DOI: 10.13266/j.issn.0252-3116.2025.16.006

Abstract

[Purpose/Significance] This paper reveals the types and levels of users’ demands for the personalized algorithm recommendation services on digital reading platforms and provides a reference for its optimization. [Method/Process] Based on semi-structured interviews, literature research, and other methods, this paper extracted the personalized algorithm recommendation services on digital reading platforms, designed a questionnaire combined with the Kano model, and identified the levels of functional demand for personalized algorithm recommendation services on digital reading platforms through Kano data analysis, mixed class analysis, and Better-Worse satisfaction index analysis and summary. On this basis, it proposed the optimization strategies. [Result/Conclusion] The personalized algorithm recommendation services on the digital reading platforms can be summarized into 31 items, among which 3 functions, such as the explanation and description of the recommendation function, belong to the necessary requirements. The function of recommending reading materials in various resource formats belongs to the expectation requirement. 6 functions, such as recommended interface layout, belong to charm requirements. 20 functions, such as real-time push and timed push, belong to the undifferentiated requirements. And the ability to collect sensitive personal information is a reverse requirement. The digital reading platform shall optimize corresponding services based on the type and level of demand for the personalized algorithm recommendation services.

Key words： digital reading; personalized algorithm recommendation; user demand; Kano model

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