Research Area:  Machine Learning
With the continuous accumulation of users check-in data, we can gradually capture users behavior patterns and mine users preferences. Based on this, the next point-of-interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users behavior habits of check-in behavior. Then, different types of context information are used to construct a personalized recommendation model. However, the users check-in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network. Therefore, we propose a category-aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check-in data, capture long-range dependence between user check-ins and get better recommendation results of POI category. We combine the spatiotemporal information of check-in data and take the POI category as users preference to train the model. Also, we develop an attention-based category-aware GRU (ATCA-GRU) model for the next POI category recommendation. The ATCA-GRU model can selectively utilize the attention mechanism to pay attention to the relevant historical check-in trajectories in the check-in sequence. We evaluate ATCA-GRU using a real-world data set, named Foursquare. The experimental results indicate that our ATCA-GRU model outperforms the existing similar methods for next POI recommendation.
Author(s) Name:  Yuwen Liu, Aixiang Pei, Fan Wang, Yihong Yang, Xuyun Zhang, Hao Wang, Hongning Dai, Lianyong Qi, Rui Ma
Journal name:  International Journal of Intelligent Systems
Publisher name:  Wiley
Volume Information:  Volume36, Issue7 July 2021 Pages 3174-3189
Paper Link:   https://onlinelibrary.wiley.com/doi/abs/10.1002/int.22412