POI recommendation is practically important to facilitate various Location-Based Social Network services, and has attracted rising research attention recently. Existing works generally assume the available POI check-ins reported by users are the ground-truth depiction of user behaviors. However, in real application scenarios, the check-in data can be rather unreliable due to both subjective and objective causes including positioning error and user privacy concerns, leading to significant negative impacts on the performance of the POI recommendation. To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network. Specifically, we construct personal POI transition graph, the semantic-based POI graph and distance-based POI graph to comprehensively model the dependencies among the POIs. As the personal POI transition graph is usually sparse and sensitive to noise, we design a Bayes-enhanced spatial dependency learning module for data augmentation from the local view. A Bayesian posterior guided graph augmentation approach is adopted to generate a new graph with collaborative signals to increase the data diversity. Then both the original and the augmented graphs are used for POI representation learning to counteract the data uncertainty issue. Next, the POI representations of the three view graphs are input into the proposed multi-view attention-based user preference learning module. By incorporating the semantic and distance correlations of POIs, the user preference can be effectively refined and finally robust recommendation results are achieved. The results of extensive experiments show that BayMAN significantly outperforms the state-of-the-art methods in POI recommendation when the available check-ins are incomplete and noisy.

翻译：POI推荐在实际中对于促进各类基于位置的社会网络服务至关重要，近年来引起了越来越多的研究关注。现有工作通常假设用户报告的可用的POI签到数据是用户行为的真实描述。然而，在真实应用场景中，由于主观和客观原因（包括定位误差和用户隐私关注），签到数据可能相当不可靠，从而对POI推荐性能产生显著的负面影响。为此，我们研究了一个新的鲁棒POI推荐问题，通过考虑用户签到的不确定性因素，提出了一种贝叶斯增强的多视图注意力网络。具体来说，我们构建了个人POI转移图、基于语义的POI图和基于距离的POI图，以全面建模POI之间的依赖关系。由于个人POI转移图通常稀疏且对噪声敏感，我们设计了一个贝叶斯增强的空间依赖学习模块，用于从局部视角进行数据增强。采用贝叶斯后验引导的图增强方法生成一个具有协作信号的新图，以增加数据多样性。然后，原始图和增强图都被用于POI表示学习，以应对数据不确定性问题。接着，三个视图图的POI表示被输入到所提出的基于多视图注意力的用户偏好学习模块中。通过融合POI的语义和距离相关性，用户偏好可以得到有效细化，最终实现鲁棒的推荐结果。大量实验结果表明，当可用签到数据不完整且存在噪声时，BayMAN在POI推荐方面显著优于当前最先进的方法。