A Meta-Path-Based Recurrent Model for Next POI Prediction with Spatial and Temporal Contexts

Xu, Hengpeng; Wu, Peizhi; Wei, Jinmao; Yang, Zhenglu; Wang, Jun

doi:10.1007/978-3-030-26075-0_17

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…The next POI prediction emphasizes the importance of sequence information. At present, Markov Chain (MC) 17 and Recurrent Neural Network (RNN) 18,19 methods have been widely applied in successive POI predictions. The MFbased methods and CF-based methods do not consider sequence information.…”

Section: Next Poi Predictionmentioning

confidence: 99%

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Bidirectional GRU networks‐based next POI category prediction for healthcare

Liu

Song

et al. 2021

Int J of Intelligent Sys

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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.

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Section: Next Poi Predictionmentioning

confidence: 99%

“…The Corona Virus Disease 2019 (COVID- 19) is a serious threat to people's lives. 1,2 People were isolated at home during the epidemic, and some people suffered from excessive pressure, anxiety, manic depression, and other emotions.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional GRU networks‐based next POI category prediction for healthcare

Liu

Song

et al. 2021

Int J of Intelligent Sys

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“…Newer methods exploit deep learning algorithms based on recurrent and/or sequential models in order to predict the next PoI such as works in [1], [12]. However these methods require a lot of data samples to give satisfactory results.…”

Section: A Poi Prediction/recommendationmentioning

confidence: 99%

High-Quality Prediction of Tourist Movements using Temporal Trajectories in Graphs

Moghtasedi

Muntean

Nardini

et al. 2020

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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In this paper, we study the problem of predicting the next position of a tourist given his history. In particular, we propose a model to identify the next point of interest that a tourist will visit in the future, by making use of similarity between trajectories on a graph and taking into account the spatial-temporal aspect of trajectories. We compare our method with a well-known machine learningbased technique, as well as with a popularity baseline, using three public real-world datasets. Our experimental results show that our technique outperforms state-of-the-art machine learning-based methods effectively, by providing at least twice more accurate results.

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“…Cui et al [55] propose a hybrid LSTM model for rich contextual learning to recognize user activity not only for the cases where a clear indicator exists in the content, but also for the ones where the activity information is latent. As the same, capturing more information from text provide a great help for Xu et al [56]. However, due to numerous RNN parameters and long sequences in data, the disk storage occupied by the above models will increase and the convergence speed of models will also slow down.…”

Section: Related Workmentioning

confidence: 99%

A Fast Lightweight Spatiotemporal Activity Prediction Method

et al. 2019

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How to predict spatiotemporal activity from geo-tagged social media is an urgent problem. Existing methods don't make full use of spatiotemporal information and text sequence features. In view of above problem, we design a Fast Lightweight Spatiotemporal Activity Prediction method(FLSAP) based on Gated Recurrent Unit(GRU) neural network. While GRU structure can extract text sequence features, the model takes up a lot of space due to the numerous parameters. At the same time, due to the long sequence in the text, the convergence speed of GRU is slow. So, we design a novel GRU neuron, GRU with Tiny and Skip(GTS), which can quickly generate a lightweight model with higher accuracy. In GTS, we add a scalar weighted residual connection to stabilize the training. Furthermore, we extend the residual connection to a gate by reusing the parameter matrices to compress the model size. At last, in order to make the model converge faster, we add a binary gate, which determine whether to skip the current state update. According to the experimental results, compared with ReAct [1] in the spatiotemporal activity prediction task, FLSAP improves the accuracy by 3.3%, reduces the model space by 98.79% and accelerates 74.4% of convergence speed.

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A Meta-Path-Based Recurrent Model for Next POI Prediction with Spatial and Temporal Contexts

Cited by 4 publications

References 29 publications

Bidirectional GRU networks‐based next POI category prediction for healthcare

Bidirectional GRU networks‐based next POI category prediction for healthcare

High-Quality Prediction of Tourist Movements using Temporal Trajectories in Graphs

A Fast Lightweight Spatiotemporal Activity Prediction Method

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