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NGPR: A comprehensive personalized point-of-interest recommendation method based on heterogeneous graphs

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Abstract

Nowadays, many people like to share the places they visited in the Location-based Social Networks (LBSNs). A Point of Interest (POI) recommendation, as one of the location-based services, helps users find new locations they prefer to visit. Recently, researchers have proposed many methods to leverage user-generated content, such as check-ins, for POI recommendation. However, due to the sparsity of user check-in information, it is still very difficult to recommend appropriate and accurate locations to users. To address the problem, in this paper, we propose a novel POI recommendation method named NGPR. Firstly, we construct a heterogeneous LBSN graph of users, POIs, categories and time periods. based on check-in records. Subsequently, the Node2Vec technique is employed to establish the latent vectors of POIs and users. Finally, we integrate comprehensive factors including the category preference, geographical distance and POI popularity for POI recommendation. The NGPR method is applied to two real LBSN datasets for experimental analysis. The experimental results show that the precision@5 of our method achieves 18.82% and 19.19% higher than that of the second best method on two real LBSN datasets respectively.

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Funding

This work was partially supported by National Natural Science Foundation of China (No. 61902096, No.61702144), Industrial Internet Innovation and Development Project of Ministry of Industry and Information Technology of China (No. TC200802G, No. TC2008033), National Natural Science Foundation of Zhejiang Province (No. LQ20F020015), and Key Research and Development Program of Zhejiang Province (No. 2020C01165).

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  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Dongjin Yu, Ting Yu, Dongjing Wang & Yi Shen

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  1. Dongjin Yu
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Correspondence to Dongjin Yu.

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Author Dongjin Yu, Author Ting Yu, Author Dongjing Wang and Author Yi Shen declare that they have no confict of interest.

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Yu, D., Yu, T., Wang, D. et al. NGPR: A comprehensive personalized point-of-interest recommendation method based on heterogeneous graphs. Multimed Tools Appl 81, 39207–39228 (2022). https://doi.org/10.1007/s11042-022-13088-4

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