  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.
