| 基于受限非负张量分解的用户社会影响力分析 |
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| 引用本文： | 魏晶晶,陈 畅,廖祥文,陈国龙,程学旗.基于受限非负张量分解的用户社会影响力分析[J].通信学报,2016,37(6):154-162. |
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| 作者姓名： | 魏晶晶 陈 畅 廖祥文 陈国龙 程学旗 |
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| 作者单位： | 1. 福州大学物理与信息工程学院，福建 福州 350116；2. 福建江夏学院电子信息科学学院，福建 福州 350108；
3. 福州大学数学与计算机科学学院，福建 福州 350116；4. 福州大学福建省网络计算与智能信息处理重点实验室，福建 福州 350116；
5. 中国科学院计算技术研究所，北京 100086 |
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| 基金项目： | 国家自然科学基金资助项目（No.61300105）；教育部博士点联合基金资助项目（No.2012351410010）；福建省科技重大专项基金资助项目（No.2013H6012）；福州市科技计划基金资助项目（No.2012-G-113, No.2013-PT-45） |
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| 摘 要： | 针对传统社会影响力分析方法未能充分考虑观点和话题信息等问题，提出了一种基于受限非负张量分解的用户社会影响力分析方法。首先把社交媒介用户相互评论关系自然地表示成三阶张量，然后通过拉普拉斯话题约束矩阵控制张量分解过程，最后根据分解得到的潜在因子度量用户观点社会影响力。该方法的优点是能有效地从受限张量分解结果中检索出给定话题下用户的社会影响力，同时保持其社会影响力的极性分布。实验结果表明，该方法的性能优于OOLAM和TwitterRank等基准算法。
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| 关 键 词： | 社会影响力 话题 观点 张量分析 |
User social influence analysis based on constrained nonnegative tensor factorization |
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| Jing-jing WEI,Chang CHEN,Xiang-wen LIAO,Guo-long CHEN,Xue-qi CHENG.User social influence analysis based on constrained nonnegative tensor factorization[J].Journal on Communications,2016,37(6):154-162. |
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| Authors: | Jing-jing WEI Chang CHEN Xiang-wen LIAO Guo-long CHEN Xue-qi CHENG |
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| Affiliation: | 1. College of Physics and Information Engineering,Fuzhou University,Fuzhou 350116,China;2. College of Electronics and Information Science,Fujian Jiangxia University,Fuzhou 350108,China;3. College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China;4. Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou University,Fuzhou 350116,China;5. Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100086,China |
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| Abstract: | Existing models for measuring user social influence fail to integrate both opinion and topic information.Therefore,a new constrained nonnegative tensor factorization method combining user’s opinion and the topical relevance was proposed.The method represented user’s comment relations as 3-order tensor,factorized the comments tensor constrained by Laplacian topical matrix,and then measures user influence according to the latent factors resulting from the tensor factorization.Thus,the new method not only was capable to effectively calculate the strength of user social influence on given topic,but also kept the polarity allocation of social influence.The experimental result shows that the performance of the proposed method is better than that of the baseline methods such as OOLAM,TwitterRank,etc. |
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| Keywords: | social influence topic opinion tensor analysis |
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