With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendation, a natural extension of POI recommendation, has attracted much attention. It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications. Existing approaches mainly model the spatial and temporal information, and memorize historical patterns through user's trajectories for recommendation. However, they suffer from the negative impact of missing and irregular check-in data, which significantly influences the model performance. In this paper, we propose an attention-based sequence-to-sequence generative model, namely POI-Augmentation Seq2Seq (PA-Seq2Seq), to address the sparsity of training set by making check-in records to be evenly-spaced. Specifically, the encoder summarises each check-in sequence and the decoder predicts the possible missing check-ins based on the encoded information. In order to learn time-aware correlation among user history, we employ local attention mechanism to help the decoder focus on a specific range of context information when predicting a certain missing check-in point. Extensive experiments have been conducted on two real-world check-in datasets, Gowalla and Brightkite, for performance and effectiveness evaluation.

中文翻译：

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用于 POI 推荐的上下文感知的基于注意力的数据增强

随着基于位置的社交网络（LBSN）的快速增长，兴趣点（POI）推荐在这十年中得到了广泛的研究。近期，POI推荐的自然延伸——下一个POI推荐备受关注。它旨在在空间和时间上下文中向用户建议下一个 POI，这在各种应用中是一项实用但具有挑战性的任务。现有的方法主要是对空间和时间信息进行建模，并通过用户的轨迹记忆历史模式进行推荐。然而，它们受到缺失和不规则签入数据的负面影响，这显着影响了模型性能。在本文中，我们提出了一种基于注意力的序列到序列生成模型，即 POI-Augmentation Seq2Seq (PA-Seq2Seq)，通过使签到记录均匀分布来解决训练集的稀疏性。具体来说，编码器汇总每个签到序列，解码器根据编码信息预测可能丢失的签到。为了学习用户历史之间的时间感知相关性，我们采用局部注意机制来帮助解码器在预测某个丢失的签到点时专注于特定范围的上下文信息。已经在两个真实世界的签到数据集 Gowalla 和 Brightkite 上进行了大量实验，用于性能和有效性评估。为了学习用户历史之间的时间感知相关性，我们采用局部注意机制来帮助解码器在预测某个丢失的签到点时专注于特定范围的上下文信息。已经在两个真实世界的签到数据集 Gowalla 和 Brightkite 上进行了大量实验，用于性能和有效性评估。为了学习用户历史之间的时间感知相关性，我们采用局部注意机制来帮助解码器在预测某个丢失的签到点时专注于特定范围的上下文信息。已经在两个真实世界的签到数据集 Gowalla 和 Brightkite 上进行了大量实验，用于性能和有效性评估。