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Multimodal Deep Belief Network Based Link Prediction and User Comment Generation

机译:基于多模式深度信任网络的链接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,而链接预测问题在于推断此类链接的价值。通过分析链路网络的结构,研究人员提出了几种解决链路预测的方法。如今,当某些成员标记其链接值时,他们也会发表评论,而很少将其用于链接预测。在本文中,通过同时考虑链接网络数据和用户评论数据,我们提出了一种基于多峰深度信任网络的链接预测方法,该方法优于其他最新方法。通过学习到的链接网络特征和用户评论特征的联合分布,我们的方法可以正确生成评论词。

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