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A data-intensive approach for discovering user similarities in social behavioral interactions based on the bayesian network

机译：一种基于贝叶斯网络的社交行为交互中发现用户相似性的数据密集型方法

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摘要

Discovering user similarities from social media can establish the basis for user targeting, product recommendation, user relationship evolution and understanding. User similarities not only depend on the topological structure but also the dependence degrees between users. In this paper, we adopt Bayesian network (BN), an important and popular probabilistic graphical model, as the underling framework and propose a data-intensive approach for discovering user similarities. First, upon the massive social behavioral interactions, we give the method for measuring direct similarities between users and the MapReduce-based algorithm for constructing a BN to describe these similarities, called user Bayesian network and abbreviated as UBN. We also give the idea for storing large-scale UBNs in a distributed file system. Then, to measure indirect similarities between users, we give the method for measuring the closeness of user connections in terms of the properties of UBN's. graphical structure. Further, we give the MapReduce-based algorithm for measuring the dependence degrees by means of UBN's probabilistic inferences. By combining the above two perspectives of measures, the indirect similarity degree between users can be achieved, while guaranteeing the applicability theoretically. Finally, we give experimental results and show the efficiency and effectiveness of our method.
机译：从社交媒体发现用户相似之处可以为用户定位，产品推荐，用户关系发展和理解奠定基础。用户相似度不仅取决于拓扑结构，还取决于用户之间的依赖程度。在本文中，我们采用一种重要且流行的概率图形模型贝叶斯网络（BN）作为底层框架，并提出了一种用于发现用户相似性的数据密集型方法。首先，基于大量的社会行为互动，我们提供了一种用于测量用户之间直接相似性的方法，以及一种基于MapReduce的算法来构造一个描述这些相似性的BN，称为用户贝叶斯网络，简称为UBN。我们还提出了在分布式文件系统中存储大型UBN的想法。然后，为了测量用户之间的间接相似性，我们提供了根据UBN的属性来测量用户连接的紧密度的方法。图形结构。此外，我们给出了基于MapReduce的算法，用于通过UBN的概率推断来测量依赖度。通过结合以上两种方法，可以在理论上保证适用性的同时，达到用户之间的间接相似度。最后，我们给出了实验结果并显示了我们方法的有效性和有效性。

著录项

  • 来源
    《Neurocomputing》 |2017年第5期|364-375|共12页
  • 作者

    Yue Kun; Wu Hao; Fu Xiaodong; Xu Juan; Yin Zidu; Liu Weiyi;

  • 作者单位

    Yunnan Univ, Sch Informat Sci & Engn, Kunming, Peoples R China;

    Yunnan Univ, Sch Informat Sci & Engn, Kunming, Peoples R China;

    Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming, Peoples R China;

    Yunnan Univ, Sch Informat Sci & Engn, Kunming, Peoples R China;

    Yunnan Univ, Sch Informat Sci & Engn, Kunming, Peoples R China;

    Yunnan Univ, Sch Informat Sci & Engn, Kunming, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Social behavioral interactions; User similarity; Bayesian network; MapReduce; Probabilistic inference;

    机译：社会行为交互;用户相似度;贝叶斯网络;MapReduce;概率推理;
  • 入库时间 2022-08-18 02:05:50

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