社交媒介已经成为了一种分享交换信息的重要平台,识别出其中影响力高的用户已经广泛地应用于推荐系统、专家识别、广告投放等应用。该文提出了一种受限张量分解方法,其能识别出给定主题下影响力高的用户,同时保持其影响力的极性分布(例如正面、中性、负面)。该方法通过拉普拉斯矩阵引入用户主题相似性约束,控制张量分解过程,使用分解结果计算用户影响力得分。实验结果表明,该方法在社会影响力分析中的性能优于OOLAM、TwitterRank等基准算法,并具有良好的可扩展性。
Abstract
Social media has become an popular platform for sharing and exchanging information. The identification of users of social influence has already been applied into many applications including recommendation systems, experts finding, social advertising et al. This paper proposes a constrained tensor factorization method to identify users with high social influence. In the factorization result, the polairy allocation of influence is preserved (i.e. positive, neutral and negative influence). This method fuses topical similarity of users by Laplacian matrix, which would control tensor factorization to approximate the user influence. Experimental results demonstrate that the method outperformes the OOLAM, TwitterRank etc. in terms of ranking accuracy.
关键词
张量分解 /
观点 /
拉普拉斯矩阵 /
社会影响力分析
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Key words
tensor factorization /
opinion /
Laplacian matrix /
social influence analysis
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参考文献
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脚注
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基金
国家自然科学基金青年项目(61300105);教育部博士点基金联合资助项目(2012351410010);福建省科技重大专项项目(2013H6012);福州市科技计划项目(2012-G-113,2013-PT-45)
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