Context-aware attention-based data augmentation for POI recommendation

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 memorise historical patterns through the user's trajectories for the recommendation. However, they suffer from the negative impact of missing and irregular check-in data, which significantly influences 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 checkin sequence and the decoder predicts the possible missing checkins based on the encoded information. In order to learn timeaware 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 realworld check-in datasets, Gowalla and Brightkite, for performance and effectiveness evaluation.