CASIA OpenIR  > 智能感知与计算研究中心
Social-Relational Topic Model for Social Networks
Guo, Weiyu; Wu, Shu; Wang, Liang; Tan, Tieniu
2015
Conference NameACM International Conference on Information and Knowledge Management (CIKM)
Source PublicationIn Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015
Conference DateOct 24-28
Conference PlaceMelbourne
AbstractSocial 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
KeywordTopic Modeling Social Networks Social Link Generation
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12335
Collection智能感知与计算研究中心
Corresponding AuthorWu, Shu
Recommended Citation
GB/T 7714
Guo, Weiyu,Wu, Shu,Wang, Liang,et al. Social-Relational Topic Model for Social Networks[C],2015.
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