Bidirectional GRU networks‐based next POI category prediction for healthcare. Issue 7 (11th October 2021)

• Record Type: Journal Article
• Authors: Liu, Yuwen; Song, Zuolong; Xu, Xiaolong; Rafique, Wajid; Zhang, Xuyun; Shen, Jun; Khosravi, Mohammad R.; Qi, Lianyong
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 7(2022)
• Page Start: 4020
• Page End: 4040
• Publication Date: 2021-10-11
• Subjects: attention mechanism -- fuzzy system -- gated recurrent unit -- health services -- POI category prediction; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22710
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point‐of‐interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check‐in. Faced with this challenge, we propose an attention‐based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check‐ins. Furthermore, since the neural network is similar to a "black box" in feature learning, the decision‐making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check‐in records, which can improve the interpretability of the model. Considering the time impact on users' check‐in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check‐in data.