Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.

中文翻译：

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使用迭代 Seq2Graph 增强为下一个 POI 推荐发现协作信号

作为基于位置的社交网络中不可或缺的组成部分，下一个兴趣点 (POI) 推荐根据用户最近的访问历史向用户推荐未探索的 POI。然而，现有工作主要将签到数据建模为孤立的 POI 序列，而忽略了来自跨序列签到信息的关键协作信号。此外，稀疏的 POI-POI 转换限制了模型学习用于推荐的有效序列模式的能力。在本文中，我们为每个 POI 序列提出了 Sequence-to-Graph (Seq2Graph) 增强，允许从属于其他序列的相关 POI 传播协作信号。然后，我们设计了一种新颖的 Sequence-to-Graph POI 推荐器 (SGRec)，它联合学习 POI 嵌入并推断用户的 s 来自图增强 POI 序列的时间偏好。为了克服 POI 级交互的稀疏性，我们通过多任务学习方案进一步将类别意识注入到 SGRec 中，该方案可捕获更密集的类别转换。因此，SGRec 充分利用协作信号来学习富有表现力的 POI 表示，并全面揭示用户偏好建模的多级序列模式。对两个真实世界数据集的大量实验证明了 SGRec 在下一个 POI 推荐中相对于最先进方法的优越性。SGRec 充分利用协作信号来学习富有表现力的 POI 表示，并且还全面揭示了用于用户偏好建模的多级序列模式。对两个真实世界数据集的大量实验证明了 SGRec 在下一个 POI 推荐中相对于最先进方法的优越性。SGRec 充分利用协作信号来学习富有表现力的 POI 表示，并且还全面揭示了用于用户偏好建模的多级序列模式。对两个真实世界数据集的大量实验证明了 SGRec 在下一个 POI 推荐中相对于最先进方法的优越性。