Sampling-based epoch differentiation calibrated graph convolution network for point-of-interest recommendation

Mo, Fan; Fan, Xin; Chen, Chongxian; Bai, Changhao; Yamana, Hayato

doi:10.1016/j.neucom.2023.127140

Neurocomputing

2024

DOI: 10.1016/j.neucom.2023.127140

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Sampling-based epoch differentiation calibrated graph convolution network for point-of-interest recommendation

Fan Mo,

Xin Fan,

Chongxian Chen

et al.

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Cited by 2 publications

References 29 publications

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A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint

Qiao,

Jiang,

Yang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint

Qiao,

Jiang,

Yang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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MCGCL: A multi-contextual graph contrastive learning-based approach for POI recommendation

Han,

Wang

2024

era

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<abstract> <p>This paper focused on the point-of-interest (POI) recommendation task. Recently, graph representation learning-based POI recommendation models have gained significant attention due to the powerful modeling capacity of graph structural data. Despite their effectiveness, we have found that recent methods struggle to effectively utilize information from POIs that have not been checked in, which could limit their performance. Hence, in this paper, we proposed a new model, named the multi-contextual graph contrastive learning (MCGCL) model, which introduces the contrastive learning into graph representation learning-based methods. First, MCGCL extracts interactions between POIs under different contextual factors from user check-in records using predefined graph structure information. Next, it samples important POI sets from different contextual factors using a random walk-based method. Then, it introduces a new contrastive learning loss that incorporates contextual information into traditional contrastive learning to enhance its ability to capture contextual information. Finally, MCGCL employs a graph neural network (GNN) model to learn representations of users and POIs. Extensive experiments on real-world datasets have demonstrated the effectiveness of MCGCL on the POI recommendation task compared to representative POI recommendation approaches.</p> </abstract>

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Sampling-based epoch differentiation calibrated graph convolution network for point-of-interest recommendation

Cited by 2 publications

References 29 publications

A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint

A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint

MCGCL: A multi-contextual graph contrastive learning-based approach for POI recommendation

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