图书情报工作

图书情报工作 >

2019 , Vol. 63 >Issue 16: 72 - 83

DOI: https://doi.org/10.13266/j.issn.0252-3116.2019.16.008

情报研究

知识找回场景下推荐系统模拟实现及评价研究

  • 程秀峰 ,
  • 张孜铭 ,
  • 孟亚琪 ,
  • 范晓莹 ,
  • 杨金庆
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  • 华中师范大学信息管理学院 武汉 430079
程秀峰(ORCID:0000-0003-2139-2122),副教授,博士,硕士生导师;张孜铭(ORCID:0000-0002-3341-5574),本科生;孟亚琪(ORCID:0000-0002-4017-7180),本科生;范晓莹(ORCID:0000-0003-0369-8312),本科生。

收稿日期: 2018-10-19

  修回日期: 2019-01-23

  网络出版日期: 2019-08-20

基金资助

本文系国家自然科学青年项目"基于QSIM的图书馆移动用户群体行为模拟与学习兴趣引导研究"（项目编号：71503097）和华中师范大学基本科研业务费资助项目"基于上下文感知技术的交互式移动学习行为研究"（项目编号：CCNU18TS039）研究成果之一。

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An Experimental Study on Simulation and Evaluation of Recommendation Systems for Knowledge Re-finding

  • Cheng Xiufeng ,
  • Zhang Ziming ,
  • Meng Yaqi ,
  • Fan Xiaoying ,
  • Yang Jinqing
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  • School of Information Management, Central China Normal University, Wuhan 430079

Received date: 2018-10-19

  Revised date: 2019-01-23

  Online published: 2019-08-20

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摘要

[目的/意义]信息过载一直是知识工作者在搜集、处理和创造知识的过程中所面临的主要困境。这种困境导致的结果之一是很难回忆起曾经使用过的文档的内容细节及具体位置，而推荐系统则能减少这样的困难。通过研究对比不同推荐系统在这一任务下的优缺点，可以帮助知识工作者更好地完成回忆任务。[方法/过程]基于相关理论，在同一场景（知识找回）模拟实现并测试了4种不同类型的推荐过程，包括基于内容的推荐CBR、基于协同过滤的推荐CFR、基于推理网络的推荐INR与融入了情境感知的推荐CAS，根据所确定的若干指标（精确性、情境相关性、预测性、多样性）对推荐效果进行比较。[结果/结论]结果显示，以上推荐系统在帮助用户回忆并找回文档过程中都有各自的优势，而基于情境感知的推荐系统在情境相关性与预测用户行为方面具有较好的效果。

关键词： 信息过载; 知识找回; 推荐系统; 情境感知

本文引用格式

程秀峰 , 张孜铭 , 孟亚琪 , 范晓莹 , 杨金庆 . 知识找回场景下推荐系统模拟实现及评价研究[J]. 图书情报工作, 2019 , 63(16) : 72 -83 . DOI: 10.13266/j.issn.0252-3116.2019.16.008

Abstract

[Purpose/significance] Information overload has been always considered as the major barrier confronted by knowledge workers in the process of gathering, processing and producing information. One of its consequences is that it is hard to recall documents that ever used, while the recommendation system could reduce such difficulty. Comparing the recommendation efficiencies through representative recommendation mechanisms may assisst knowledge workers in accomplishing the task of knowledge re-finding.[Method/process] Based on associated recommendation system theoies, this paper presents a simulation on 4 different recommendation procedures in an unified experimental scene(knowledge re-finding), the precedures includes CBR, CFR, INR and CAS. 4 evaluation criteria (precision, context-relevance, action-prediction, diversity) has been used to evaluate and compare the efficiency of corresponding recommendation systems.[Result/conclusion] The results show that each recommendation procedure has its own advantages in knowledge re-finding from different perspectives, and CAS has advantages in both context-relevance and action-prediction.

Key words： information overload; knowledge re-finding; recommendation system; context-awareness

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