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Context-aware user preferences prediction on location-based social networks

机译：基于位置的社交网络上的上下文感知用户首选项预测

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摘要

Recently, the increasing number of mobile users in location-based social networks (LBSNs) has generated large amounts of data, which provides unprecedented opportunities to study mobile user preferences for location recommendation. However, the huge amount of LBSNs data and sparsity problem limited improvements of efficiency and accuracy on mobile user preferences for location recommendation. This paper proposes a context-aware user preferences prediction algorithm for location recommendation on LBSNs. It introduces cloud model and category information into estimating the similarity of users and locations. Furthermore, it predicts user preferences of new locations according to the categories of new locations and user visited. In particular, the algorithm is parallelized with MapReduce framework for significant improvement in efficiency. Experimental results on Foursquare dataset demonstrate the performance gains of the algorithm.
机译：最近，基于位置的社交网络（LBSNS）中的移动用户数量越来越多地产生了大量数据，这提供了前所未有的机会来研究移动用户偏好的位置推荐。然而，大量LBSNS数据和稀疏问题有限地改善了移动用户偏好对于位置推荐的效率和准确性。本文提出了一种关于LBSN上的位置建议的上下文感知用户偏好预测算法。它将云模型和类别信息引入估算用户和位置的相似性。此外，根据新位置和用户访问的类别，它预测了新位置的用户偏好。特别地，该算法与MapReduce框架并行化以显着提高效率。 Foursquare DataSet上的实验结果证明了算法的性能增益。

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  • 来源
    《Journal of Intelligent Information Systems》 |2019年第1期|51-67|共17页
  • 作者

    Wang Fan; Meng Xiangwu; Zhang Yujie;

  • 作者单位

    Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China|Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China;

    Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China|Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China;

    Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China|Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Context-aware user preferences; Category information; Social ties; Geographical proximity; Cloud model;

    机译：上下文感知用户偏好;类别信息;社交关系;地理邻近;云模型;

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