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数据分析与知识发现  2017, Vol. 1 Issue (6): 22-35     https://doi.org/10.11925/infotech.2096-3467.2017.06.03
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
社会化推荐研究进展与发展趋势演化*——基于文献计量和社会网络分析的视角
李飞, 张健(), 王宗水
北京信息科技大学经济管理学院 北京 100192
绿色发展大数据决策北京市重点实验室 北京 100192
Review of Social Recommendation with Bibliometrics and Social Network Analysis
Li Fei, Zhang Jian(), Wang Zongshui
School of Economics&Management, Beijing Information Science&Technology University, Beijing 100192, China
Beijing Key Lab of Green Development Decision Based on Big Data, Beijing 100192, China
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摘要 

目的】从文献计量和社会网络分析的角度对社会化推荐进行内容特征及网络演化的研究, 归纳领域研究热点和发展趋势。【方法】以WoS数据库为数据源, 采用人工判读法、关键词共现、文献计量、社会网络分析及数据可视化等方法对样本数据进行数据挖掘和关联分析。【结果】检索到社会化推荐类文献3 701篇, 论文数量整体呈上升趋势, 以发文量阈值为阶段划分标准, 将社会化推荐的发展演化趋势划分为三个阶段, 各阶段研究特征明显。【局限】仅以关键词为探究文献内容特征的依据, 内容深度挖掘相对不足, 其中阶段划分是为了分析研究内容及演化趋势的变化, 并不存在统一的划分标准。【结论】我国学者在该领域的国际影响力逐年上升, 领域研究内容方面阶段性变化特征明显, 社会化媒介、协同过滤等传统研究方向一直保持较高关注度。

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李飞
张健
王宗水
关键词 社会化推荐研究进展发展趋势社会网络分析文献计量    
Abstract

[Objective] This paper summarizes the content characteristics and network evolution of social recommendation research based on the of bibliometrics and social network analysis. [Methods] First, we collected the data of social recommendation research from the Web of Science database. Then we analyzed the data with manual interpretation, keywords co-occurrence analysis, bibliometrics, social network analysis and data visualization. [Results] A total of 3701 articles on social recommendation were retrieved, which have been increasing recently. Based on the threshold of papers published each year, we divided the development of social recommendation research into three distinct stages. [Limitations] We only used keywords to explore the characteristics of the relevant document contents, which could be improved with in-depth text mining. There is lack of uniform criterion to classify the evolution stages of the related research. Our study only shows the changing of contents and development trends. [Conclusions] The international impacts of Chinese scholars have been rising in social recommendation studies, which highly focus on the topics of social media and collaborative filtering.

Key wordsSocial Recommendation    Research Progress    Development Trend    Social Network Analysis    Bibliometrics
收稿日期: 2017-03-22      出版日期: 2017-08-25
ZTFLH:  TP393  
基金资助:*本文系科技创新服务能力建设项目“北京市知识管理研究基地”(项目编号: 71F1710913)和促进高校内涵发展-学科建设类项目“管理科学与工程一级学科建设”(项目编号: 5121723500)的研究成果之一
引用本文:   
李飞, 张健, 王宗水. 社会化推荐研究进展与发展趋势演化*——基于文献计量和社会网络分析的视角[J]. 数据分析与知识发现, 2017, 1(6): 22-35.
Li Fei,Zhang Jian,Wang Zongshui. Review of Social Recommendation with Bibliometrics and Social Network Analysis. Data Analysis and Knowledge Discovery, 2017, 1(6): 22-35.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.06.03      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I6/22
  全球整体论文量和各国按年份的趋势
  各国论文量地理分布(1997-2016)
序号 期刊 出现频次 发文百分比 影响因子 立即指数 国家
1 EXPERT SYSTEMS WITH APPLICATIONS 52 1.393 2.981 0.938 USA
2 INFORMATION SCIENCES 38 1.018 1.832 0.500 USA
3 KNOWLEDGE-BASED SYSTEMS 31 0.83 1.702 0.291 NETHERLANDS
4 NEUROCOMPUTING 29 0.777 2.392 0.563 NETHERLANDS
5 MULTIMEDIA TOOLS AND APPLICATIONS 29 0.777 1.331 0.125 NETHERLANDS
6 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA
ENGINEERING
27 0.723 2.476 0.257 USA
7 ONLINE INFORMATION REVIEW 25 0.67 1.152 0.077 ENGLAND
8 DECISION SUPPORT SYSTEMS 25 0.67 2.604 0.246 NETHERLANDS
9 IEEE TRANSACTIONS ON MULTIMEDIA 21 0.562 1.152 0.077 USA
10 JOURNAL OF UNIVERSAL COMPUTER SCIENCE 20 0.536 0.546 0.048 AUSTRIA
11 MULTIMEDIA SYSTEMS 19 0.509 1.410 0.349 GERMANY
12 JOURNAL OF INFORMATION SCIENCE 17 0.455 2.604 0.246 ENGLAND
13 KNOWLEDGE AND INFORMATION SYSTEMS 16 0.428 1.702 0.291 ENGLAND
14 JOURNAL OF THE AMERICAN SOCIETY FOR
INFORMATION SCIENCE AND TECHNOLOGY
15 0.402 2.452 - USA
15 JOURNAL OF MANAGEMENT INFORMATION
SYSTEMS
15 0.402 3.025 0.098 USA
  全球发文量前15的期刊
序号 阶段一 阶段二 阶段三
关键词 词频 关键词 词频 关键词 词频
1 recommender system 120 recommender system 134 recommender system 131
2 social network 65 social network 89 social network 76
3 collaborative filtering 37 social media/collaborative
filtering
37 collaborative filtering 49
4 personalization 29 algorithms 36 social media 33
5 trust 26 social network analysis 26 location based social network 29
6 social network analysis 19 social tagging 22 algorithms 18
7 information retrieval/
experimentation
18 data mining 21 topic modeling 16
8 social tagging/data mining/
algorithms/ontology
17 trust 18 social recommendation/ matrix
factorization
15
9 NLP 16 Personalization/Twitter 16 Twitter/Personalization/data
mining/location recommendations
13
10 evaluation 15 experimentation 15 online social networks 12
频次累计百分比 15.88% 13.27% 20.14%
  三个阶段关键词词频统计
阶段一 推荐系统 社会网络 阶段二 推荐系统 社会网络 阶段三 推荐系统 社会网络
关键词 词频 共现系数 关键词 词频 共现系数 关键词 词频 共现系数
协同过滤 37 0.41 0.06 社会化媒介 37 0.04 0.07 协同过滤 49 0.42 0.16
个性化 29 0.34 0.09 协同过滤 37 0.38 0.30 社会化媒介 33 0.12 0.04
信誉 26 0.18 0.26 算法 26 0.22 0.14 基于位置的
社会网络
29 0.08 0.06
社会网络分析 19 0.13 0 社会网络
分析
22 0.13 0 算法 18 0.04 0.08
信息检索 18 0.04 0.06 社会标签 21 0.15 0.02 主题建模 16 0.09 0.06
实验法 18 0.17 0.12 数据挖掘 19 0.10 0.04 社会化推荐 15 0.11 0.09
社会标签 17 0.38 0 信誉 18 0.08 0.22 矩阵分解 15 0.25 0.06
数据挖掘 17 0 0.03 个性化 16 0.13 0.16 Twitter 13 0.07 0.03
算法 17 0.15 0.21 Twitter 16 0.13 0.05 个性化 13 0.17 0
本体 17 0.07 0.06 实验法 15 0.11 0.05 位置推荐 13 0.07 0.03
自然语言处理 16 0.04 0.04 性能 14 0.14 0.11 数据挖掘 13 0.12 0.1
评估 15 0.05 0 聚类 14 0.07 0 在线社交
网络
12 0.1 0
标签 14 0.15 0.10 自然语言
处理
13 0.02 0 实验法 11 0.05 0.07
社会化
媒介
13 0.15 0.07 社会化推荐 12 0 0.03 社会影响 10 0.08 0.07
互联网 12 0.05 0.07 信息检索 12 0.02 0 好友推荐 9 0.03 0.11
机器学习 12 0.07 0.05 本体 12 0.12 0 链路预测 9 0.06 0.08
人为因素 11 0.06 0.11 群推荐 11 0.03 0.06 微博 9 0.09 0.04
P2P 11 0.08 0.19 评估 11 0.05 0.03 社会网络
分析
9 0.09 0
信息提取 11 0.06 0.07 主题建模 10 0 0.03 性能 9 0.06 0
信誉 10 0.06 0.31 大众分类 10 0.11 0 随机游走 9 0.03 0.11
合计 340 2.64 1.90 合计 313 2.03 1.31 合计 314 2.13 1.22
  三个阶段推荐系统和社会网络共现系数统计
  关键词中心性层次分布网络(阶段一)
  关键词中心性层次分布网络(阶段二)
  关键词中心性层次分布网络(阶段三)
网络特性指标 阶段一 阶段二 阶段三
网络密度 0.162 0.2122 0.2476
集聚系数 0.4039 0.4141 0.3865
网络平均度 18.1429 25.4667 23.2766
  三个阶段关键词网络结构特性指标统计
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