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An efficient latent-factor-based approach to social relationship recommendation

机译：一种有效的基于潜在因素的社会关系推荐方法

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摘要

Social relationship recommenders aim at predicting potential useful relationships with high accuracy and efficiency, which is critically important in social network services for addressing information overload. Existing relationship recommenders mostly emphasize on friend recommendation in online social networks, which can not satisfy the requirements of industrial information systems. This work proposes an efficient latent-factor (LF)-based approach to predict multicategory relationships rather than only friendship in a social network. The main idea is to construct multicategory relationship data and develop the corresponding recommenders. To do so, two dimensions are designed for social relationship data, i.e., a category dimension built on real social relationship types, and an extended dimension built on the involved persons. Depending on the two dimensional relationship data, we construct a rating matrix by analyzing user preferences to each social relationship category one belonged to. For analyzing the resultant rating matrix with high accuracy, the hill-climbing and extended-linear-biases-enhanced latent factor (HC-ELBLF) model is proposed. The original grid-search-based learning process in the original ELBLF is substituted by the hill-climbing algorithm, which is an efficient and practical greed algorithm for parameter selection. The experimental results on two industrial datasets show effectiveness of the proposed HC-ELBLF approach.
机译：社交关系推荐者旨在准确高效地预测潜在的有用关系，这对于解决信息过载的社交网络服务至关重要。现有的关系推荐者大多强调在线社交网络中的朋友推荐，这不能满足工业信息系统的要求。这项工作提出了一种有效的基于潜在因子（LF）的方法来预测多类别关系，而不仅仅是社交网络中的友谊。主要思想是构造多类别关系数据并开发相应的推荐者。为此，为社交关系数据设计了两个维度，即基于真实社交关系类型的类别维度和基于所涉及人员的扩展维度。根据二维关系数据，我们通过分析用户对每个所属的社会关系类别的偏好来构建评分矩阵。为了高精度地分析结果评分矩阵，提出了爬山和线性线性偏置增强潜在因子（HC-ELBLF）模型。原始ELBLF中基于网格搜索的原始学习过程被爬山算法所替代，该算法是一种有效且实用的用于参数选择的贪婪算法。在两个工业数据集上的实验结果表明了所提出的HC-ELBLF方法的有效性。

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