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A Prediction Method of Mobile User Preference Based on the Influence between Users

机译:基于用户之间影响的移动用户偏好预测方法

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摘要

User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user's influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.
机译:用户首选项将受到其他用户的影响。为了准确地预测移动用户的偏好,将用户之间的影响引入用户偏好的预测模型。首先,根据移动用户的交互行为来构建移动社交网络,并根据所构建的移动社交网络的拓扑和移动用户行为来计算用户的影响。其次,根据用户的影响力,用户之间的交互行为以及用户偏好的相似度来计算用户之间的影响力。在基于交互行为计算影响力时,要考虑上下文信息;在基于用户偏好的相似度计算影响力时,要考虑上下文信息和用户偏好的顺序。然后,基于获得的用户之间的影响,采用改进的协作过滤方法来预测移动用户的偏好。最后,对真实数据集和集成数据集进行了实验,结果表明,与现有方法相比,该方法可以获得更准确的移动用户偏好。

著录项

  • 来源
    《International journal of digital multimedia broadcasting》 |2018年第2018期|8081409.1-8081409.12|共12页
  • 作者单位

    Institute of Computer Science and Information Engineering, Tianjin University of Science & Technology, Tianjin, China;

    Institute of Computer Science and Information Engineering, Tianjin University of Science & Technology, Tianjin, China;

    Institute of Computer Science and Information Engineering, Tianjin University of Science & Technology, Tianjin, China;

    Institute of Computer Science and Information Engineering, Tianjin University of Science & Technology, Tianjin, China;

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