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Multimodal Deep Belief Network Based Link Prediction and User Comment Generation

机译:基于多模式的深度信仰网络的链路预测和用户评论生成

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In social network services, the relationship among members can be represented as link network, and the link prediction problem is to infer the value of such links. By analysing the structure of link network, researchers have proposed several methods for solving link prediction. Nowdays, when some members label their link values, they also make comments, which have been seldom considered for link prediction. In this paper, by considering both the link network data and user comment data, we propose multimodal deep belief network based link prediction method, which outperforms other state-of-art methods. With the learned joint distribution of link network features and user comment features, our method could generate comment words properly.
机译:在社交网络服务中,成员之间的关系可以表示为链接网络,链路预测问题是推断出这样的链路的值。通过分析链路网络的结构,研究人员提出了几种用于解决链路预测的方法。现在,当一些成员标记他们的链接值时,他们还发表评论,这很少考虑链接预测。在本文中,通过考虑链接网络数据和用户评论数据,我们提出了多模式的深度信念基于网络的链路预测方法,其优于其他最先进的方法。通过了解到链接网络功能和用户评论功能的联合分布,我们的方法可以正确生成评论单词。

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