Social-Relational Topic Model for Social Networks

CASIA OpenIR > 模式识别实验室

	Social-Relational Topic Model for Social Networks
	Guo, Weiyu; Wu, Shu; Wang, Liang; Tan, Tieniu
	2015
会议名称	ACM International Conference on Information and Knowledge Management (CIKM)
会议录名称	In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015
会议日期	Oct 24-28
会议地点	Melbourne
摘要	Social networking services, such as Twitter and Sina Weibo, have tremendous popularity in recent years. Mass of short texts and social links are aggregated into these service platforms. To realize personalized services on social network, topic inference from both short texts and social links plays more and more important role. Most conventional topic modeling methods focus on analyzing formal texts, e.g., papers, news and blogs, and usually assume that the links are only generated by topical factors. As a result, on social network, the learned topics of these methods are usually affected by topic-irrelevant links. Recently, a few approaches use artificial priors to recognize the links generated by the popularity factor in topic modeling. However, employing global priors, these methods can not well capture the distinct properties of each link and still suffer from the effect of topic-irrelevant links. To address the above limitations, we propose a novel Social-Relational Topic Model (SRTM), which can alleviate the effect of topic-irrelevant links by analyzing relational users’ topics of each link. SRTM jointly models texts and social links for learning the topic distribution and topical influence of each user. The experimental results show that, our model outperforms the state-of-thearts in topic modeling and social link prediction
关键词	Topic Modeling Social Networks Social Link Generation
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12335
专题	模式识别实验室
通讯作者	Wu, Shu
推荐引用方式 GB/T 7714	Guo, Weiyu,Wu, Shu,Wang, Liang,et al. Social-Relational Topic Model for Social Networks[C],2015.

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