Next Point-of-interest (POI) recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial diversity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world location-based social networks (LBSNs) datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.

Next and next new point-of-interest (POI) recommendation are essential instruments in promoting customer experiences and business operations related to locations. However, due to the sparsity of the check-in records, they still remain insufficiently studied. In this article, we propose to utilize personalized latent behavior patterns learned from contextual features, e.g., time of day, day of week, and location category, to improve the effectiveness of the recommendations. Two variations of models are developed, including GPDM, which learns a fixed pattern distribution for all users; and PPDM, which learns personalized pattern distribution for each user. In both models, a soft-max function is applied to integrate the personalized Markov chain with the latent patterns, and a sequential Bayesian Personalized Ranking (S-BPR) is applied as the optimization criterion. Then, Expectation Maximization (EM) is in charge of finding optimized model parameters. Extensive experiments on three large-scale commonly adopted real-world LBSN data sets prove that the inclusion of location category and latent patterns helps to boost the performance of POI recommendations. Specifically, our models in general significantly outperform other state-of-the-art methods for both next and next new POI recommendation tasks. Moreover, our models are capable of making accurate recommendations regardless of the short/long duration or distance.