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題名：社會化推薦研究進展與發展趨勢演化--基於文獻計量和社會網絡分析的視角
書刊名：數據分析與知識發現
作者：李飛／張健／王宗水
出版日期：2017
卷期：2017(6)
頁次：22-35
主題關鍵詞：社會化推薦；研究進展；發展趨勢；社會網絡分析；文獻計量；Social recommendation；Research progress；Development trend；Social network analysis；Bibliometrics
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【目的】從文獻計量和社會網絡分析的角度對社會化推薦進行內容特征及網絡演化的研究,歸納領域研究熱點和發展趨勢。【方法】以Wo S數據庫為數據源,采用人工判讀法、關鍵詞共現、文獻計量、社會網絡分析及數據可視化等方法對樣本數據進行數據挖掘和關聯分析。【結果】檢索到社會化推薦類文獻3 701篇,論文數量整體呈上升趨勢,以發文量閾值為階段劃分標準,將社會化推薦的發展演化趨勢劃分為三個階段,各階段研究特征明顯。【局限】僅以關鍵詞為探究文獻內容特征的依據,內容深度挖掘相對不足,其中階段劃分是為了分析研究內容及演化趨勢的變化,并不存在統一的劃分標準。【結論】我國學者在該領域的國際影響力逐年上升,領域研究內容方面階段性變化特征明
[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.
期刊論文
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13.Jäschke, R.、Marinho, L.、Hotho, A.(2008)。Tag Recommendations in Social Bookmarking Systems。AI Communications，21(4)，231-247。  new window
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20.Sun, Z. B.、Han, L. X.、Huang, W. L.(2015)。Recommender Systems Based on Social Networks。Journal of Systems and Software，99(C)，109-119。  new window
21.Baik, J.、Lee, K.、Lee, S.(2016)。Predicting Personality Traits Related to Consumer Behavior Using SNS Analysis。New Review of Hypermedia and Multimedia，22(3)，189-206。  new window
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會議論文
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4.Rendle, S.、Marinho, L. B.、Nanopoulos, A.(2009)。Learning Optimal Ranking with Tensor Factorization for Tag Recommendation。The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France。New York：ACM。727-736。  new window
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6.Golbeck, J.(2006)。Generating Predictive Movie Recommendations from Trust in Social Networks。The International Conference on Trust Management, Pisa, Italy。Springer。93-104。  new window
7.Siersdorfer, S.、Sizov, S.(2009)。Social Recommender Systems for Web 2.0 Folksonomies。The 20th ACM Conference on Hypertext and Hypermedia。New York：ACM。261-270。  new window
8.Ma, H.、Zhou, D.、Liu, C.(2011)。Recommender Systems with Social Regularization。The 4th International Conference on Web Search and Web Data Mining, Hong Kong, China。ACM。287-296。  new window
9.King, I.、Lyu, M. R.、Ma, H.(2010)。Introduction to Social Recommendation。The 19th International Conference on World Wide Web。New York：ACM。1355-1356。  new window
10.Ma, H.、King, I.、Lyu, M.(2009)。Learning to Recommend with Social Trust Ensemble。The 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval。New York：ACM。203-210。  new window
11.Jamali, M.、Ester, M.(2010)。A Matrix Factorization Technique with Trust Propagations for Recommendation in Social Networks。The 4th ACM Conference on Recommender Systems。New York：ACM。135-142。  new window
12.Jamali, M.、Ester, M.(2009)。TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation。The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining。New York：ACM。397-406。  new window
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14.Hannon, J.、Bennett, M.、Smyth, B.(2011)。Recommending Twitter Users to Follow Using Content and Collaborative Filtering Approaches。The 4th ACM Conference on Recommender Systems。New York：ACM。199-206。  new window
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其他
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