bidirectional gru networks based next poi category prediction for healthcare CORD-Papers-2022-06-02 (Version 1)

Title: Bidirectional GRU networks-based next POI category prediction for healthcare
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. 2021 Wiley Periodicals LLC
Published: 2021
Journal: International Journal of Intelligent Systems
DOI: 10.1002/int.22710
DOI_URL: http://doi.org/10.1002/int.22710
Author Name: Liu Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Song Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/song_z
Author Name: Xu X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/xu_x
Author Name: Rafique W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rafique_w
Author Name: Zhang X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_x
Author Name: Shen J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/shen_j
Author Name: Khosravi M R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/khosravi_m_r
Author Name: Qi L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/qi_l
license: unk
license_url: [unknown license]
source_x: WHO
source_x_url: https://www.who.int/
who_covidence_id: #covidwho-1473144
url: https://doi.org/10.1002/int.22710
has_full_text: FALSE
G_ID: bidirectional_gru_networks_based_next_poi_category_prediction_for_healthcare