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首页> 外文会议>Proceedings of the Twenty-Third international joint conference on artificial intelligence >Where You Like to Go Next:Successive Point-of-Interest Recommendation
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Where You Like to Go Next:Successive Point-of-Interest Recommendation

机译：您想下一步去哪里：成功的兴趣点推荐

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摘要

Personalized point-of-interest (POI) recommendation is a significant task in location-based social networks (LBSNs) as it can help provide better user experience as well as enable third-party services,e.g.,launching advertisements.To provide a good recommendation,various research has been conducted in the literature.However,pervious efforts mainly consider the “check-ins” in a whole and omit their temporal relation.They can only recommend POI globally and cannot know where a user would like to go tomorrow or in the next few days.In this paper,we consider the task of successive personalized POI recommendation in LBSNs,which is a much harder task than standard personalized POI recommendation or prediction.To solve this task,we observe two prominent properties in the check-in sequence: personalized Markov chain and region localization.Hence,we propose a novel matrix factorization method,namely FPMCLR,to embed the personalized Markov chains and the localized regions.Our proposed FPMC-LR not only exploits the personalized Markov chain in the check-in sequence,but also takes into account users’ movement constraint,i.e.,moving around a localized region.More importantly,utilizing the information of localized regions,we not only reduce the computation cost largely,but also discard the noisy information to boost recommendation.Results on two real-world LBSNs datasets demonstrate the merits of our proposed FPMC-LR.
机译：个性化兴趣点（POI）推荐在基于位置的社交网络（LBSN）中是一项重要任务，因为它可以帮助提供更好的用户体验以及启用第三方服务，例如发布广告。 ，文献中进行了各种研究。但是，以往的努力主要是从整体上考虑“签到”，而忽略了它们的时间关系。他们只能在全球范围内推荐POI，而无法知道用户明天或将来要去哪里。接下来的几天。本文考虑了LBSN中连续个性化POI推荐的任务，这比标准个性化POI推荐或预测要困难得多。为解决此任务，我们在签到中观察到两个突出的特性序列：个性化马尔可夫链和区域定位。因此，我们提出了一种新颖的矩阵分解方法，即FPMCLR，以嵌入个性化马尔可夫链和局部区域。不仅在签到顺序中利用了个性化的马尔可夫链，而且还考虑了用户的移动约束，即在局部区域内移动。更重要的是，利用局部区域的信息，我们不仅大大降低了计算成本，但也丢弃了嘈杂的信息以增强推荐。两个真实世界的LBSN数据集的结果证明了我们提出的FPMC-LR的优点。

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  • 来源
  • 会议地点 Beijing(CN)
  • 作者

    Chen Cheng; Haiqin Yang; Michael R.Lyu; Irwin King;

  • 作者单位

    Shenzhen Research Institute,The Chinese University of Hong Kong,Shenzhen,China;

    Department of Computer Science and Engineering The Chinese University of Hong Kong,Shatin,N.T.,Hong Kong;

    Shenzhen Research Institute,The Chinese University of Hong Kong,Shenzhen,China;

    Department of Computer Science and Engineering The Chinese University of Hong Kong,Shatin,N.T.,Hong Kong;

    Shenzhen Research Institute,The Chinese University of Hong Kong,Shenzhen,China;

    Department of Computer Science and Engineering The Chinese University of Hong Kong,Shatin,N.T.,Hong Kong;

    Shenzhen Research Institute,The Chinese University of Hong Kong,Shenzhen,China;

    Department of Computer Science and Engineering The Chinese University of Hong Kong,Shatin,N.T.,Hong Kong;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;
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