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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (6): 22-35    DOI: 10.11925/infotech.2096-3467.2017.06.03
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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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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 words： Social Recommendation      Research Progress      Development Trend      Social Network Analysis      Bibliometrics     
Received: 22 March 2017      Published: 25 August 2017
ZTFLH:  TP393  
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Fei Li
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Cite this article:

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.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.06.03     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I6/22

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
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%
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
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
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