Point of interest (POI) recommendation can benefit users and merchants. It is a very important and popular service in modern life. In this paper, we aim to study the next new POI recommendation problem with the consideration of privacy preserving in edge computing. The challenge lies in capturing the transition patterns between POIs precisely and meanwhile protecting users’ location. In this paper, first, we propose to model users’ check-in sequences with their latent states based on HMM, and EM algorithm is used to estimate the parameters of the model. Second, we propose to protect users’ location information by a weighted noise injection method. Third, we predict users’ next movement according to his current location based on Forward algorithm. Experimental results on two large-scale LBSNs datasets show that our proposed model without noise injection can achieve better recommendation accuracy than several state-of-the-art techniques, and the proposed weighted noise injection approach can achieve better performance on privacy preserving than traditional one with a little cost on accuracy.

中文翻译：

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在针对移动边缘计算的下一个POI建议中提供隐私保护

兴趣点（POI）推荐可以使用户和商家受益。这是现代生活中非常重要且受欢迎的服务。在本文中，我们旨在研究下一个新的POI推荐问题，同时考虑边缘计算中的隐私保护。挑战在于精确捕获POI之间的过渡模式，同时保护用户的位置。在本文中，首先，我们建议基于HMM对用户的签入序列及其潜在状态进行建模，并使用EM算法估计模型的参数。其次，我们建议通过加权噪声注入方法来保护用户的位置信息。第三，基于Forw​​ard算法，根据用户当前位置预测用户的下一步运动。