基于微博兴趣相似度的研究
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摘要
微博用户构成了一个社交网络,在这个结构中,各用户之间又相互联系,存在着关系上的相似性。本文针对微博中信息量大,用户之间兴趣上的某种相似性,提出了一种多态相似度模型。从不同方面综合考虑,通过用户背景,交互性,以及微博内容之间的相似性,将用户兴趣形似性加权结合得到最终的结果模型。实验结果表明,多态相似度模型较传统的方法,在用户个性化推荐中更准确地反映用户的兴趣。
Weibo users constitute a social network. In this structure, users are connected to each other and there is a similar relationship. This paper proposes a polymorphic similarity model for the similarity of interest in microblogs and the interest among users.From a variety of aspects, through the user background, interactivity, and the similarity between the content of the Weibo, the user interest form weights are combined to obtain the final result model. The experimental results show that the polymorphic similarity model reflects the user's interest more accurately in the user's personalized recommendation than the traditional method.
Weibo users constitute a social network. In this structure, users are connected to each other and there is a similar relationship. This paper proposes a polymorphic similarity model for the similarity of interest in microblogs and the interest among users.From a variety of aspects, through the user background, interactivity, and the similarity between the content of the Weibo, the user interest form weights are combined to obtain the final result model. The experimental results show that the polymorphic similarity model reflects the user's interest more accurately in the user's personalized recommendation than the traditional method.
引文
[1]HUANG Xianying,CHEN Hongyang,LIU Yingtao.Research on Microblog short text similarity and its application in Microblog topic detection[J].
[2]Xu zhi-Ming,LiDong,Liu Ting,et al.Measuring similarity between microblog users and its application[J].Chinese Journal of Computers.2014,37(1):207-218(in Chinese)
[3]HUANG Hongcheng,LU Weijin,et al.User Relationships Prediction Algorithm with Interest Similarity Measurement[J].Computer Science and Exploration.2017,11(7)-1068-12
[4]Wang Xiao-Yu,Xiong Fang,Ling Bo,Zhou Aoying.A similarity-based algorithm for topic exploration and distillation.Journal of Software,2003,14(9):1578-1585(in Chinese)
[5]邢千里,刘列,刘奕群,张敏,马少平.微博中用户标签的研究[J].软件学报,2015,26(7):1626-1637.
[6]彭泽环,孙乐,韩先培,石贝.基于排序学习的微博用户推荐[J].中文信息学报,2013,27(4):96-102.
[7]杨圩生,罗爱民,张萌萌.基于信任环的用户冷启动推荐[J].计算机科学,2013,40(11a):363-366.
[8]张园美.微博用户兴趣分析方法应用研究[D].大连:大连理工大学,2015.
[9]李峰,侯加英,曾荣仁,等.融合词向量的多特征句子相似度计算方法研究[J].计算机科学与技术,2017(11).
[2]Xu zhi-Ming,LiDong,Liu Ting,et al.Measuring similarity between microblog users and its application[J].Chinese Journal of Computers.2014,37(1):207-218(in Chinese)
[3]HUANG Hongcheng,LU Weijin,et al.User Relationships Prediction Algorithm with Interest Similarity Measurement[J].Computer Science and Exploration.2017,11(7)-1068-12
[4]Wang Xiao-Yu,Xiong Fang,Ling Bo,Zhou Aoying.A similarity-based algorithm for topic exploration and distillation.Journal of Software,2003,14(9):1578-1585(in Chinese)
[5]邢千里,刘列,刘奕群,张敏,马少平.微博中用户标签的研究[J].软件学报,2015,26(7):1626-1637.
[6]彭泽环,孙乐,韩先培,石贝.基于排序学习的微博用户推荐[J].中文信息学报,2013,27(4):96-102.
[7]杨圩生,罗爱民,张萌萌.基于信任环的用户冷启动推荐[J].计算机科学,2013,40(11a):363-366.
[8]张园美.微博用户兴趣分析方法应用研究[D].大连:大连理工大学,2015.
[9]李峰,侯加英,曾荣仁,等.融合词向量的多特征句子相似度计算方法研究[J].计算机科学与技术,2017(11).