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首页> 外文会议>International conference on web information systems engineering >Learning Social Relationship Strength via Matrix Co-Factorization with Multiple Kernels
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Learning Social Relationship Strength via Matrix Co-Factorization with Multiple Kernels

机译：通过矩阵协同分解学习社会关系强度，具有多个内核

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摘要

In recent years the research on measuring relationship strength among the people in a social network has gained attention due to its potential applications of social network analysis. The challenge is how we can learn social relationship strength based on various resources such as user profiles and social interactions. In this paper we propose a KPMCF model to learn social relationship strength based on users' latent features inferred from both profile and interaction information. The proposed model takes an uniformed approach of integrating Matrix Co-Factorization with Multiple Kernels. We conduct experiments on real-world data sets for typical web mining applications, showing that the proposed model produces better relationship strength measurement in comparison with other social factors.
机译：近年来，由于社会网络分析潜在应用，衡量社会网络中人民关系的力量的研究。挑战是我们如何根据用户简档和社交互动等各种资源来学习社会关系实力。在本文中，我们提出了一种KPMCF模型，以了解基于用户潜在的思维和交互信息推断的社会关系强度。该模型采用均匀的方法来与多个内核集成矩阵协同分解。我们对典型网站挖掘应用进行真实数据集进行实验，表明所提出的模型与其他社会因素相比，提出的模型产生了更好的关系强度测量。

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