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Tsinghua Science and Technology 2021, 26(2): 208-218 https://doi.org/10.26599/TST.2019.9010059
Open Access | Issue | Published: 24 July 2020

POI Neural-Rec Model via Graph Embedding Representation

Show Author's Information Kang Yang,  Jinghua Zhu( ),  Xu Guo
Department of Computer Science, Heilongjiang University, Harbin 150080.
Keywords:
deep learning, neural networks, Point-Of-Interest (POI) recommendation, graph embedding, Deepwalk, Location-Based Social Networks (LBSNs)
Cite this article:
Yang K, Zhu J, Guo X. POI Neural-Rec Model via Graph Embedding Representation. Tsinghua Science and Technology, 2021, 26(2): 208-218. https://doi.org/10.26599/TST.2019.9010059
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Abstract Full text About this article

With the booming of the Internet of Things (IoT) and the speedy advancement of Location-Based Social Networks (LBSNs), Point-Of-Interest (POI) recommendation has become a vital strategy for supporting people’s ability to mine their POIs. However, classical recommendation models, such as collaborative filtering, are not effective for structuring POI recommendations due to the sparseness of user check-ins. Furthermore, LBSN recommendations are distinct from other recommendation scenarios. With respect to user data, a user’s check-in record sequence requires rich social and geographic information. In this paper, we propose two different neural-network models, structural deep network Graph embedding Neural-network Recommendation system (SG-NeuRec) and Deepwalk on Graph Neural-network Recommendation system (DG-NeuRec) to improve POI recommendation. combined with embedding representation from social and geographical graph information (called SG-NeuRec and DG-NeuRec). Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the framework of the neural network. Finally, we compare the performances of these two models and analyze the reasons for their differences. Results from comprehensive experiments on two real LBSNs datasets indicate the effective performance of our model.


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About this article

POI Neural-Rec Model via Graph Embedding Representation

Show Author's information Kang Yang, Jinghua Zhu( ), Xu Guo
Department of Computer Science, Heilongjiang University, Harbin 150080.

Abstract

With the booming of the Internet of Things (IoT) and the speedy advancement of Location-Based Social Networks (LBSNs), Point-Of-Interest (POI) recommendation has become a vital strategy for supporting people’s ability to mine their POIs. However, classical recommendation models, such as collaborative filtering, are not effective for structuring POI recommendations due to the sparseness of user check-ins. Furthermore, LBSN recommendations are distinct from other recommendation scenarios. With respect to user data, a user’s check-in record sequence requires rich social and geographic information. In this paper, we propose two different neural-network models, structural deep network Graph embedding Neural-network Recommendation system (SG-NeuRec) and Deepwalk on Graph Neural-network Recommendation system (DG-NeuRec) to improve POI recommendation. combined with embedding representation from social and geographical graph information (called SG-NeuRec and DG-NeuRec). Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the framework of the neural network. Finally, we compare the performances of these two models and analyze the reasons for their differences. Results from comprehensive experiments on two real LBSNs datasets indicate the effective performance of our model.

Keywords: deep learning, neural networks, Point-Of-Interest (POI) recommendation, graph embedding, Deepwalk, Location-Based Social Networks (LBSNs)

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Received: 04 October 2019
Revised: 10 November 2019
Accepted: 13 December 2019
Published: 24 July 2020
Issue date: April 2021

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