...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of mobile computing and multimedia communications >Location Tracking Prediction of Network Users Based on Online Learning Method With Python
【24h】

Location Tracking Prediction of Network Users Based on Online Learning Method With Python

机译：基于Python在线学习方法的网络用户位置跟踪预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Aiming at the problem that the precision and recall rate of traditional prediction methods are low and its low prediction efficiency, a Python-based trajectory tracking prediction method of online learning network user location is proposed. First, troubleshooting terminal programs of online learning network user by programming in Python (computer programming language) structure, the location trajectory data of online learning network user is spatially processed. In this way, features of time-related, spatial correlation, social relationship correlation, and user preference characteristics are extracted respectively to realize feature normalization processing. Second, on this basis, the cosine similarity is used to calculate the similarity of user behavior trajectory. According to K-MEANS (hard clustering algorithm), the time dimension is considered. Finally, the clustering result of users' behavior trajectory based on the sign-in data is compared with a preset threshold to predict the online user location trajectory. The experimental results show that the proposed method normalizes the user's trajectory, combines the time segment, and compares it with the preset threshold, which does not only improve the prediction efficiency but also obtains higher and more feasible precision and recall rate.
机译：针对传统预测方法的精度和召回率低，预测效率低的问题，提出了一种基于Python的在线学习网络用户位置轨迹跟踪预测方法。首先，通过使用Python（计算机编程语言）结构编程对在线学习网络用户的终端程序进行故障排除，对在线学习网络用户的位置轨迹数据进行空间处理。这样，分别提取时间相关，空间相关，社交关系相关和用户偏好特征，以实现特征归一化处理。其次，在此基础上，使用余弦相似度来计算用户行为轨迹的相似度。根据K-MEANS（硬聚类算法），考虑了时间维度。最后，将基于登录数据的用户行为轨迹的聚类结果与预设阈值进行比较，以预测在线用户位置轨迹。实验结果表明，该方法对用户轨迹进行了归一化处理，结合了时间段，并将其与预设阈值进行比较，不仅提高了预测效率，而且获得了越来越高的可行精度和召回率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号