计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 185-190.doi: 10.11896/j.issn.1002-137X.2019.05.028
苏畅, 彭劭闻, 谢显中, 刘宁宁
SU Chang, PENG Shao-wen, XIE Xian-zhong, LIU Ning-ning
摘要： 基于位置的社交网络(Location-Based Social Networks,LBSN)为用户提供基于位置的服务,允许移动用户在社交网络中共享各自的位置以及与位置相关的信息。签到预测研究已经成为LBSN的重要且非常具有挑战性的任务。目前的预测技术大部分集中在以用户为中心的签到预测研究,而针对兴趣点签到预测的研究很少。文中主要研究以特定兴趣点为中心的签到预测。由于数据存在极端稀疏性的问题,运用传统的模型很难从数据中挖掘出用户的潜在签到规律。针对以特定兴趣点为中心的签到预测问题,提出了一种结合因子分解机和深度学习的新型网络模型(TSWNN),该模型融合了时间特征、空间特征、天气特征,基于因子分解机的思想处理高维稀疏向量,并采用全连接的隐藏层以挖掘用户在兴趣点的潜在签到行为模式,预测特定兴趣点的签到情况。在两个经典的LBSN数据集Gowalla和Brightkite上的实验结果表明了所提模型的优越性能。
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