Next location recommendation plays an important role in various location-based services, yielding great value for both users and service providers. Existing methods usually model temporal dependencies with explicit time intervals or learn representation from customized point of interest (POI) graphs with rich context information to capture the sequential patterns among POIs. However, this problem is perceptibly complex, because various factors, e.g., users’ preferences, spatial locations, time contexts, activity category semantics, and temporal relations, need to be considered together, while most studies lack sufficient consideration of the collaborative signals. Toward this goal, we propose a novel Multi-Level Collaborative Neural Network for next location Recommendation (MCN4Rec). Specifically, we design a multi-level view representation learning with level-wise contrastive learning to collaboratively learn representation from local and global perspectives to capture complex heterogeneous relationships among user, POI, time, and activity categories. Then, a causal encoder-decoder is applied to the learned representations of check-in sequences to recommend the next location. Extensive experiments on four real-world check-in mobility datasets demonstrate that our model significantly outperforms the existing state-of-the-art baselines for the next location recommendation. Ablation study further validates the benefits of the collaboration of the designed sub-modules. The source code is available at https://github.com/quai-mengxiang/MCN4Rec.

下一个位置推荐在各种基于位置的服务中发挥着重要作用，为用户和服务提供商都带来了巨大的价值。现有方法通常使用明确的时间间隔对时间依赖性进行建模，或者从具有丰富上下文信息的定制兴趣点 (POI) 图中学习表示，以捕获 POI 之间的顺序模式。然而，这个问题显然很复杂，因为需要一起考虑各种因素，例如用户的偏好、空间位置、时间上下文、活动类别语义和时间关系，而大多数研究缺乏对协作信号的充分考虑。为了这个目标，我们提出了一部小说中号多层次C协作神经氮下一个地点的网络记录建议（MCN4Rec）。具体来说，我们设计了一种具有逐级对比学习的多级视图表示学习，从局部和全局角度协作学习表示，以捕获用户、POI、时间和活动类别之间复杂的异构关系。然后，将因果编码器-解码器应用于签到序列的学习表示以推荐下一个位置。对四个现实世界签到移动数据集的广泛实验表明，我们的模型显着优于下一个位置推荐的现有最先进基线。消融研究进一步验证了所设计的子模块协作的好处。源代码可在 https://github.com/quai-mengxiang/MCN4Rec 获取。