推荐系统领域研究现状分析
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摘要
[目的/意义]旨在为促进推荐系统领域研究向纵深发展提供参考。[方法/过程]以Web of Science数据库中1997—2017年收录的推荐系统主题的文献数据为研究对象,借助可视化分析软件HistCite和Pajek,从出版年、核心机构、核心作者、核心期刊、核心文献、引文时序等角度分析推荐系统领域的研究现状。[结果/结论]推荐系统领域是一个热门的交叉研究领域;推荐系统领域已出现一批核心机构、核心作者、核心期刊以及核心文献;推荐系统领域的研究问题日趋复杂、目标日渐多元、研究方法日益多样。
[Purpose/significance] The paper is to provide reference for promoting recommender systems research developing in depth and breadth. [Method/process] The paper takes the documents on recommender systems collected in Web of Science published in 1997—2017 as research objects, by the visual softwares of HistCite and Pajek, analyzes the research status from the aspects of publication year, core institutions, core authors, core journals, core documents, and citation sequences. [Result/conclusion] It is found that:(1) The field of recommender system is a hot cross-disciplinary research field;(2) A number of core institutions, core authors,core journal, and core documents have emerged in this field;(3) The research issues in the field of recommended systems are becoming increasingly complex, with increasingly diversified objectives and increasingly diverse research methods.
[Purpose/significance] The paper is to provide reference for promoting recommender systems research developing in depth and breadth. [Method/process] The paper takes the documents on recommender systems collected in Web of Science published in 1997—2017 as research objects, by the visual softwares of HistCite and Pajek, analyzes the research status from the aspects of publication year, core institutions, core authors, core journals, core documents, and citation sequences. [Result/conclusion] It is found that:(1) The field of recommender system is a hot cross-disciplinary research field;(2) A number of core institutions, core authors,core journal, and core documents have emerged in this field;(3) The research issues in the field of recommended systems are becoming increasingly complex, with increasingly diversified objectives and increasingly diverse research methods.
引文
[1]刘建国,周涛,汪秉宏.个性化推荐系统的研究进展[J].自然科学进展,2009,19(1):1-15.
[2]王国霞,刘贺平.个性化推荐系统综述[J].计算机工程与应用,2012,48(7):66-76.
[3]弗朗西斯科·里奇.推荐系统:技术、评估及高效算法[M].北京:机械工业出版社,2015.
[4]奉国和,梁晓婷.国内推荐引擎学术研究知识图谱分析[J].情报科学,2012,30(1):144-148,160.
[5]孙鸿飞,武慧娟,李晟光.基于知识图谱的个性化推荐研究热点与前沿分析[J].情报科学,2014,32(12):12-15,20.
[6]黄文彬,张惟恺,徐扬.基于作者共引分析的推荐系统研究知识图谱构建[J].现代情报,2015,35(11):3-12,17.
[7]陈悦,刘则渊.悄然兴起的科学知识图谱[J].科学学研究,2005,23(2):149-154.
[8]陈悦,陈超美,刘则渊,等. CiteSpace知识图谱的方法论功能[J].科学学研究,2015,33(2):242-253.
[9]赵蓉英,吴胜男,陈瑞.基于引证关系的知识转移模式及其实证研究:以知识管理研究为例[J].情报杂志,2013(3):137-143.
[10]田军.信息可视化分析工具的比较分析:以CiteSpace、HistCite和RefViz为例[J].图书馆学研究,2014(14):90-95.
[11] EUGENE G,PUDOVKIN A I,ISTOMIN V S. Why do we need algorithmic historiography?[J]. Journal of the American Society for Information Science&Technology,2003,54(5):400-412.
[12] GARFIELD E. Historiographic mapping of knowledge domains literature[J]. Journal of Information Science,2004,30(2):119-145.
[13]冯昌扬.基于HistCite的社交媒体领域引文脉络分析[J].图书馆学研究,2015(5):22-29.
[14]穆亚凤,都平平,齐近图,等.基于引文分析工具的学科发展态势研究[J].现代情报,2017,37(3):126-131.
[15] SUN Y,GRIMES S. The emerging dynamic structure of national innovation studies:a bibliometric analysis[J].Scientometrics,2016,106(1):1-24.
[16]董克,刘德洪,江洪,等.基于主路径分析的HistCite结果改进研究[J].情报理论与实践,2011,34(3):113-116.
[17] LEYDESDORFF L,THOR A,BORNMANN L. Further steps in integrating the platforms of WoS and Scopus:Historiography with HistCiteTM and main-path analysis[J]. El Profesional De La Información,2017,26(4):662-671.
[18] DE NOOY W,MRVAR A,BATAGELJ V. Exploratory Social Network Analysis with Pajek:Revised and Expanded Edition for Updated Software[M]. Third Edition. New York:Cambridge University Press:2011.
[19] ADOMAVICIUS G,TUZHILIN A. Toward the Next Generation of Recommender Systems:A Survey of the State-ofthe-Art and Possible Extensions[J]. IEEE Transactions on Knowledge&Data Engineering,2005,17(6):734-749.
[20] HERLOCKER J L,KONSTAN J A,TERVEEN L G,et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems(TOIS),2004,22(1):5-53.
[21] BOBADILLA J,ORTEGA F,HERNANDO A,et al.Recommender systems survey[J]. Knowledge-based systems,2013(46):109-132.
[22] LU L,MEDO M,YEUNG C H,et al. Recommender systems[J]. Physics Reports,2012,519(1):1-49.
[23] NILASHI M,IBRAHIM O B,ITHNIN N. Hybrid recommendation approaches for multi-criteria collaborative filtering[J]. Expert Systems with Applications,2014,41(8):3879-3900.
[24] MORADI P,AHMADIAN S. A reliability-based recommendation method to improve trust-aware recommender systems[J]. Expert Systems with Applications,2015,42(21):7386-7398.
[25] LOGESH R,SUBRAMANIYASWAMY V. A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users:[J]. Comput Intell Neurosci,2016(2):28.
[26] KERMANY N R,ALIZADEH S H. A hybrid multicriteria recommender system using ontology and neuro-fuzzy techniques[J]. Electronic Commerce Research&Applications,2017(21):50-64.
[27] SARWAR B,KARYPIS G,KONSTAN J,et al. Itembased collaborative filtering recommendation algorithms[C].Hong Kong:ACM,2001:285-295.
[28] RESNICK P,IACOVOU N,SUCHAK M,et al. GroupLens:an open architecture for collaborative filtering of netnews[C]. North Carolina:ACM,1994:175-186.
[29] BREESE J S,HECKERMAN D,KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C]. Wisconsin:Morgan Kaufmann Publishers Inc,1998:43-52.
[30] LINDEN G,SMITH B,YORK J. Amazon. com Recommendations:Item-to-Item Collaborative Filtering[J]. IEEE Internet Computing,2003,7(1):76-80.
[31] BALABANOVIC M,SHOHAM Y. Fab:contentbased,collaborative recommendation[J]. Communication of the Acm,1997,40(3):66-72.
[32] RESNICK P,VARIAN H R. Recommender systems[J]. Communications of the ACM,1997,40(3):56-58
[33] MOBASHER B,COOLEY R,SRIVASTAVA J. Automatic personalization based on web usage mining[J]. Communications of the ACM,2000,43(8):142-151.
[34] GOLDBERG K,ROEDER T,GUPTA D,et al. Eigentaste:A constant time collaborative filtering algorithm[J]. information retrieval,2001,4(2):133-151.
[35] DESHPANDE M,KARYPIS G. Item-based top-n recommendation algorithms[J]. ACM Transactions on Information Systems(TOIS),2004,22(1):143-177.
[36] ZHANG Z K,ZHOU T,ZHANG Y C. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs[J]. Physica A Statistical Mechanics&Its Applications,2009,389(1):179-186.
[37] ZHOU T,PARISI G. Solving the apparent diversityaccuracy dilemma of recommender systems[J]. Proceedings of the National Academy of Sciences of the United States of America,2010,107(10):4511.
[38] BOBADILLA J,ORTEGA F,HERNANDO A. Recommender systems survey[J]. Knowledge-Based Systems,2013,46(1):109-132.
[39] LU J,WU D,MAO M,et al. Recommender system application developments[J]. Decision Support Systems,2015(74):12-32.
[40] ZHANG Z,LIU Y,XU G,et al. A Weighted Adaptation Method on Learning User Preference Profile[J]. Knowledge-Based Systems,2016(112):114-126.
[41] DENG X,ZHONG Y,LU L,et al. A general and effective diffusion-based recommendation scheme on coupled social networks[J]. Information Sciences,2017(417):420-434.
[42] SOHRABI B,TOLOO M,MOEINI A,et al. Evaluation of recommender systems:A multi-criteria decision making approach[J]. Iranian Journal of Management Studies,2015(8):589-605.
[2]王国霞,刘贺平.个性化推荐系统综述[J].计算机工程与应用,2012,48(7):66-76.
[3]弗朗西斯科·里奇.推荐系统:技术、评估及高效算法[M].北京:机械工业出版社,2015.
[4]奉国和,梁晓婷.国内推荐引擎学术研究知识图谱分析[J].情报科学,2012,30(1):144-148,160.
[5]孙鸿飞,武慧娟,李晟光.基于知识图谱的个性化推荐研究热点与前沿分析[J].情报科学,2014,32(12):12-15,20.
[6]黄文彬,张惟恺,徐扬.基于作者共引分析的推荐系统研究知识图谱构建[J].现代情报,2015,35(11):3-12,17.
[7]陈悦,刘则渊.悄然兴起的科学知识图谱[J].科学学研究,2005,23(2):149-154.
[8]陈悦,陈超美,刘则渊,等. CiteSpace知识图谱的方法论功能[J].科学学研究,2015,33(2):242-253.
[9]赵蓉英,吴胜男,陈瑞.基于引证关系的知识转移模式及其实证研究:以知识管理研究为例[J].情报杂志,2013(3):137-143.
[10]田军.信息可视化分析工具的比较分析:以CiteSpace、HistCite和RefViz为例[J].图书馆学研究,2014(14):90-95.
[11] EUGENE G,PUDOVKIN A I,ISTOMIN V S. Why do we need algorithmic historiography?[J]. Journal of the American Society for Information Science&Technology,2003,54(5):400-412.
[12] GARFIELD E. Historiographic mapping of knowledge domains literature[J]. Journal of Information Science,2004,30(2):119-145.
[13]冯昌扬.基于HistCite的社交媒体领域引文脉络分析[J].图书馆学研究,2015(5):22-29.
[14]穆亚凤,都平平,齐近图,等.基于引文分析工具的学科发展态势研究[J].现代情报,2017,37(3):126-131.
[15] SUN Y,GRIMES S. The emerging dynamic structure of national innovation studies:a bibliometric analysis[J].Scientometrics,2016,106(1):1-24.
[16]董克,刘德洪,江洪,等.基于主路径分析的HistCite结果改进研究[J].情报理论与实践,2011,34(3):113-116.
[17] LEYDESDORFF L,THOR A,BORNMANN L. Further steps in integrating the platforms of WoS and Scopus:Historiography with HistCiteTM and main-path analysis[J]. El Profesional De La Información,2017,26(4):662-671.
[18] DE NOOY W,MRVAR A,BATAGELJ V. Exploratory Social Network Analysis with Pajek:Revised and Expanded Edition for Updated Software[M]. Third Edition. New York:Cambridge University Press:2011.
[19] ADOMAVICIUS G,TUZHILIN A. Toward the Next Generation of Recommender Systems:A Survey of the State-ofthe-Art and Possible Extensions[J]. IEEE Transactions on Knowledge&Data Engineering,2005,17(6):734-749.
[20] HERLOCKER J L,KONSTAN J A,TERVEEN L G,et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems(TOIS),2004,22(1):5-53.
[21] BOBADILLA J,ORTEGA F,HERNANDO A,et al.Recommender systems survey[J]. Knowledge-based systems,2013(46):109-132.
[22] LU L,MEDO M,YEUNG C H,et al. Recommender systems[J]. Physics Reports,2012,519(1):1-49.
[23] NILASHI M,IBRAHIM O B,ITHNIN N. Hybrid recommendation approaches for multi-criteria collaborative filtering[J]. Expert Systems with Applications,2014,41(8):3879-3900.
[24] MORADI P,AHMADIAN S. A reliability-based recommendation method to improve trust-aware recommender systems[J]. Expert Systems with Applications,2015,42(21):7386-7398.
[25] LOGESH R,SUBRAMANIYASWAMY V. A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users:[J]. Comput Intell Neurosci,2016(2):28.
[26] KERMANY N R,ALIZADEH S H. A hybrid multicriteria recommender system using ontology and neuro-fuzzy techniques[J]. Electronic Commerce Research&Applications,2017(21):50-64.
[27] SARWAR B,KARYPIS G,KONSTAN J,et al. Itembased collaborative filtering recommendation algorithms[C].Hong Kong:ACM,2001:285-295.
[28] RESNICK P,IACOVOU N,SUCHAK M,et al. GroupLens:an open architecture for collaborative filtering of netnews[C]. North Carolina:ACM,1994:175-186.
[29] BREESE J S,HECKERMAN D,KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C]. Wisconsin:Morgan Kaufmann Publishers Inc,1998:43-52.
[30] LINDEN G,SMITH B,YORK J. Amazon. com Recommendations:Item-to-Item Collaborative Filtering[J]. IEEE Internet Computing,2003,7(1):76-80.
[31] BALABANOVIC M,SHOHAM Y. Fab:contentbased,collaborative recommendation[J]. Communication of the Acm,1997,40(3):66-72.
[32] RESNICK P,VARIAN H R. Recommender systems[J]. Communications of the ACM,1997,40(3):56-58
[33] MOBASHER B,COOLEY R,SRIVASTAVA J. Automatic personalization based on web usage mining[J]. Communications of the ACM,2000,43(8):142-151.
[34] GOLDBERG K,ROEDER T,GUPTA D,et al. Eigentaste:A constant time collaborative filtering algorithm[J]. information retrieval,2001,4(2):133-151.
[35] DESHPANDE M,KARYPIS G. Item-based top-n recommendation algorithms[J]. ACM Transactions on Information Systems(TOIS),2004,22(1):143-177.
[36] ZHANG Z K,ZHOU T,ZHANG Y C. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs[J]. Physica A Statistical Mechanics&Its Applications,2009,389(1):179-186.
[37] ZHOU T,PARISI G. Solving the apparent diversityaccuracy dilemma of recommender systems[J]. Proceedings of the National Academy of Sciences of the United States of America,2010,107(10):4511.
[38] BOBADILLA J,ORTEGA F,HERNANDO A. Recommender systems survey[J]. Knowledge-Based Systems,2013,46(1):109-132.
[39] LU J,WU D,MAO M,et al. Recommender system application developments[J]. Decision Support Systems,2015(74):12-32.
[40] ZHANG Z,LIU Y,XU G,et al. A Weighted Adaptation Method on Learning User Preference Profile[J]. Knowledge-Based Systems,2016(112):114-126.
[41] DENG X,ZHONG Y,LU L,et al. A general and effective diffusion-based recommendation scheme on coupled social networks[J]. Information Sciences,2017(417):420-434.
[42] SOHRABI B,TOLOO M,MOEINI A,et al. Evaluation of recommender systems:A multi-criteria decision making approach[J]. Iranian Journal of Management Studies,2015(8):589-605.